code: 
  '''simple docstring'''
  
  
  
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  __A          =    logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  __A          =    {
      "base": AutoModel,
      "sequence-classification": AutoModelForSequenceClassification,
      "question-answering": AutoModelForQuestionAnswering,
      "pretraining": AutoModelForPreTraining,
      "token-classification": AutoModelForTokenClassification,
      "language-modeling": AutoModelWithLMHead,
      "summarization": AutoModelForSeqaSeqLM,
      "translation": AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  __A          =    {
      "linear": get_linear_schedule_with_warmup,
      "cosine": get_cosine_schedule_with_warmup,
      "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
      "polynomial": get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  __A          =    sorted(arg_to_scheduler.keys())
  __A          =    "{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  
  
  
  
  class 			A							(       pl.LightningModule				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=None       ,     lowerCamelCase__="base"       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       ,     **lowerCamelCase__       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__()
  									# TODO: move to self.save_hyperparameters()
  									# self.save_hyperparameters()
  									# can also expand arguments into trainer signature for easier reading
  
  									self.save_hyperparameters(lowerCamelCase__       )
  									lowercase__							 =       0
  									lowercase__							 =       Path(self.hparams.output_dir       )
  									lowercase__							 =       self.hparams.cache_dir if self.hparams.cache_dir else None
  									if config is None:
  															lowercase__							 =       AutoConfig.from_pretrained(
  															    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path       ,     **({"""num_labels""": num_labels} if num_labels is not None else {})       ,     cache_dir=lowerCamelCase__       ,     **lowerCamelCase__       ,     )
  									else:
  															lowercase__							 =       config
  
  									lowercase__							 =       ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  									for p in extra_model_params:
  															if getattr(self.hparams       ,     lowerCamelCase__       ,     lowerCamelCase__       ):
  																					assert hasattr(self.config       ,     lowerCamelCase__       ), F'''model config doesn\'t have a `{p}` attribute'''
  																					setattr(self.config       ,     lowerCamelCase__       ,     getattr(self.hparams       ,     lowerCamelCase__       )       )
  
  									if tokenizer is None:
  															lowercase__							 =       AutoTokenizer.from_pretrained(
  															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,     cache_dir=lowerCamelCase__       ,     )
  									else:
  															lowercase__							 =       tokenizer
  									lowercase__							 =       MODEL_MODES[mode]
  									if model is None:
  															lowercase__							 =       self.model_type.from_pretrained(
  															    self.hparams.model_name_or_path       ,     from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path       )       ,     config=self.config       ,     cache_dir=lowerCamelCase__       ,     )
  									else:
  															lowercase__							 =       model
  
  
  
  			def A__ (					self       ,     *lowerCamelCase__       ,     **lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_type.from_pretrained(*lowerCamelCase__       ,     **lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       arg_to_scheduler[self.hparams.lr_scheduler]
  									lowercase__							 =       get_schedule_func(
  									    self.opt       ,     num_warmup_steps=self.hparams.warmup_steps       ,     num_training_steps=self.total_steps()       )
  									lowercase__							 =       {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  									return scheduler
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model
  									lowercase__							 =       ["""bias""", """LayerNorm.weight"""]
  									lowercase__							 =       [
  									    {
  									        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
  									        """weight_decay""": self.hparams.weight_decay,
  									    },
  									    {
  									        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )],
  									        """weight_decay""": 0.0,
  									    },
  									]
  									if self.hparams.adafactor:
  															lowercase__							 =       Adafactor(
  															    lowerCamelCase__       ,     lr=self.hparams.learning_rate       ,     scale_parameter=lowerCamelCase__       ,     relative_step=lowerCamelCase__       )
  
  									else:
  															lowercase__							 =       AdamW(
  															    lowerCamelCase__       ,     lr=self.hparams.learning_rate       ,     eps=self.hparams.adam_epsilon       )
  									lowercase__							 =       optimizer
  
  									lowercase__							 =       self.get_lr_scheduler()
  
  									return [optimizer], [scheduler]
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.validation_step(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.validation_end(lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       max(1       ,     self.hparams.gpus       )  # TODO: consider num_tpu_cores
  									lowercase__							 =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if stage == "test":
  															lowercase__							 =       len(self.test_dataloader().dataset       )
  									else:
  															lowercase__							 =       self.get_dataloader("""train"""       ,     self.hparams.train_batch_size       ,     shuffle=lowerCamelCase__       )
  															lowercase__							 =       len(self.train_dataloader().dataset       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = False       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									raise NotImplementedError("""You must implement this for your task"""       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.train_loader
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.get_dataloader("""dev"""       ,     self.hparams.eval_batch_size       ,     shuffle=lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.get_dataloader("""test"""       ,     self.hparams.eval_batch_size       ,     shuffle=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return os.path.join(
  									    self.hparams.data_dir       ,     """cached_{}_{}_{}""".format(
  									        lowerCamelCase__       ,     list(filter(lowerCamelCase__       ,     self.hparams.model_name_or_path.split("""/"""       )       )       ).pop()       ,     str(self.hparams.max_seq_length       )       ,     )       ,     )
  
  
  
  			@pl.utilities.rank_zero_only
  			def A__ (					self       ,     lowerCamelCase__       )			->		None:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.output_dir.joinpath("""best_tfmr"""       )
  									lowercase__							 =       self.step_count
  									self.model.save_pretrained(lowerCamelCase__       )
  									self.tokenizer.save_pretrained(lowerCamelCase__       )
  
  
  
  			@staticmethod
  			def A__ (					lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									parser.add_argument(
  									    """--model_name_or_path"""       ,     default=lowerCamelCase__       ,     type=lowerCamelCase__       ,     required=lowerCamelCase__       ,     help="""Path to pretrained model or model identifier from huggingface.co/models"""       ,     )
  									parser.add_argument(
  									    """--config_name"""       ,     default=""""""       ,     type=lowerCamelCase__       ,     help="""Pretrained config name or path if not the same as model_name"""       )
  									parser.add_argument(
  									    """--tokenizer_name"""       ,     default=lowerCamelCase__       ,     type=lowerCamelCase__       ,     help="""Pretrained tokenizer name or path if not the same as model_name"""       ,     )
  									parser.add_argument(
  									    """--cache_dir"""       ,     default=""""""       ,     type=lowerCamelCase__       ,     help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""       ,     )
  									parser.add_argument(
  									    """--encoder_layerdrop"""       ,     type=lowerCamelCase__       ,     help="""Encoder layer dropout probability (Optional). Goes into model.config"""       ,     )
  									parser.add_argument(
  									    """--decoder_layerdrop"""       ,     type=lowerCamelCase__       ,     help="""Decoder layer dropout probability (Optional). Goes into model.config"""       ,     )
  									parser.add_argument(
  									    """--dropout"""       ,     type=lowerCamelCase__       ,     help="""Dropout probability (Optional). Goes into model.config"""       ,     )
  									parser.add_argument(
  									    """--attention_dropout"""       ,     type=lowerCamelCase__       ,     help="""Attention dropout probability (Optional). Goes into model.config"""       ,     )
  									parser.add_argument("""--learning_rate"""       ,     default=5e-5       ,     type=lowerCamelCase__       ,     help="""The initial learning rate for Adam."""       )
  									parser.add_argument(
  									    """--lr_scheduler"""       ,     default="""linear"""       ,     choices=lowerCamelCase__       ,     metavar=lowerCamelCase__       ,     type=lowerCamelCase__       ,     help="""Learning rate scheduler"""       ,     )
  									parser.add_argument("""--weight_decay"""       ,     default=0.0       ,     type=lowerCamelCase__       ,     help="""Weight decay if we apply some."""       )
  									parser.add_argument("""--adam_epsilon"""       ,     default=1e-8       ,     type=lowerCamelCase__       ,     help="""Epsilon for Adam optimizer."""       )
  									parser.add_argument("""--warmup_steps"""       ,     default=0       ,     type=lowerCamelCase__       ,     help="""Linear warmup over warmup_steps."""       )
  									parser.add_argument("""--num_workers"""       ,     default=4       ,     type=lowerCamelCase__       ,     help="""kwarg passed to DataLoader"""       )
  									parser.add_argument("""--num_train_epochs"""       ,     dest="""max_epochs"""       ,     default=3       ,     type=lowerCamelCase__       )
  									parser.add_argument("""--train_batch_size"""       ,     default=32       ,     type=lowerCamelCase__       )
  									parser.add_argument("""--eval_batch_size"""       ,     default=32       ,     type=lowerCamelCase__       )
  									parser.add_argument("""--adafactor"""       ,     action="""store_true"""       )
  
  
  
  
  
  class 			A							(       pl.Callback				):
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if (
  									    trainer.is_global_zero and trainer.global_rank == 0
  									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
  															pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  
  
  
  
  class 			A							(       pl.Callback				):
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       trainer.lr_schedulers[0]["""scheduler"""]
  									lowercase__							 =       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
  									pl_module.logger.log_metrics(lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									rank_zero_info("""***** Validation results *****"""       )
  									lowercase__							 =       trainer.callback_metrics
  									# Log results
  									for key in sorted(lowerCamelCase__       ):
  															if key not in ["log", "progress_bar"]:
  																					rank_zero_info("""{} = {}\n""".format(lowerCamelCase__       ,     str(metrics[key]       )       )       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									rank_zero_info("""***** Test results *****"""       )
  									lowercase__							 =       trainer.callback_metrics
  									# Log and save results to file
  									lowercase__							 =       os.path.join(pl_module.hparams.output_dir       ,     """test_results.txt"""       )
  									with open(lowerCamelCase__       ,     """w"""       ) as writer:
  															for key in sorted(lowerCamelCase__       ):
  																					if key not in ["log", "progress_bar"]:
  																											rank_zero_info("""{} = {}\n""".format(lowerCamelCase__       ,     str(metrics[key]       )       )       )
  																											writer.write("""{} = {}\n""".format(lowerCamelCase__       ,     str(metrics[key]       )       )       )
  
  
  
  
  
  def  _A    (							lowercase__		,		lowercase__	):
  						#  To allow all pl args uncomment the following line
  						#  parser = pl.Trainer.add_argparse_args(parser)
  						parser.add_argument(
  						    """--output_dir"""		,		default=lowercase__		,		type=lowercase__		,		required=lowercase__		,		help="""The output directory where the model predictions and checkpoints will be written."""		,		)
  						parser.add_argument(
  						    """--fp16"""		,		action="""store_true"""		,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""		,		)
  
  						parser.add_argument(
  						    """--fp16_opt_level"""		,		type=lowercase__		,		default="""O2"""		,		help=(
  						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
  						        """See details at https://nvidia.github.io/apex/amp.html"""
  						    )		,		)
  						parser.add_argument("""--n_tpu_cores"""		,		dest="""tpu_cores"""		,		type=lowercase__	)
  						parser.add_argument("""--max_grad_norm"""		,		dest="""gradient_clip_val"""		,		default=1.0		,		type=lowercase__		,		help="""Max gradient norm"""	)
  						parser.add_argument("""--do_train"""		,		action="""store_true"""		,		help="""Whether to run training."""	)
  						parser.add_argument("""--do_predict"""		,		action="""store_true"""		,		help="""Whether to run predictions on the test set."""	)
  						parser.add_argument(
  						    """--gradient_accumulation_steps"""		,		dest="""accumulate_grad_batches"""		,		type=lowercase__		,		default=1		,		help="""Number of updates steps to accumulate before performing a backward/update pass."""		,		)
  						parser.add_argument("""--seed"""		,		type=lowercase__		,		default=42		,		help="""random seed for initialization"""	)
  						parser.add_argument(
  						    """--data_dir"""		,		default=lowercase__		,		type=lowercase__		,		required=lowercase__		,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""		,		)
  
  
  
  
  
  
  
  def  _A    (							lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=True		,		lowercase__=None		,		lowercase__=[]		,		lowercase__=None		,		lowercase__=None		,		**lowercase__		,		):
  						pl.seed_everything(args.seed	)
  
  						# init model
  						lowercase__							 =       Path(model.hparams.output_dir	)
  						odir.mkdir(exist_ok=lowercase__	)
  
  						# add custom checkpoints
  						if checkpoint_callback is None:
  												lowercase__							 =       pl.callbacks.ModelCheckpoint(
  												    filepath=args.output_dir		,		prefix="""checkpoint"""		,		monitor="""val_loss"""		,		mode="""min"""		,		save_top_k=1	)
  						if early_stopping_callback:
  												extra_callbacks.append(lowercase__	)
  						if logging_callback is None:
  												lowercase__							 =       LoggingCallback()
  
  						lowercase__							 =       {}
  
  						# TODO: remove with PyTorch 1.6 since pl uses native amp
  						if args.fpaa:
  												lowercase__							 =       16
  												# train_params["amp_level"] = args.fp16_opt_level
  
  						if args.gpus > 1:
  												lowercase__							 =       """auto"""  # "ddp"
  												lowercase__							 =       """ddp"""
  
  						lowercase__							 =       args.accumulate_grad_batches
  						lowercase__							 =       None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
  						lowercase__							 =       """auto"""
  
  						lowercase__							 =       pl.Trainer.from_argparse_args(
  						    lowercase__		,		weights_summary=lowercase__		,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]		,		logger=lowercase__		,		**lowercase__		,		)
  
  						if args.do_train:
  												trainer.fit(lowercase__	)
  
  						return trainer
  
  
  
  
  
  
  
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          =    torch.cuda.is_available()
  __A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  __A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __A          =    {
      "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-0_8,
      "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  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       """\n""".join(lowercase__	)
  						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						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							(       __UpperCAmelCase				):
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls
  
  
  
  			@require_torch_multi_gpu
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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__       )
  
  
  
  
  
  
  