code: 
  
  
  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
  
  
  SCREAMING_SNAKE_CASE : Optional[int]          =    logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  SCREAMING_SNAKE_CASE : Dict          =    {
      "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
  SCREAMING_SNAKE_CASE : List[str]          =    {
      "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
  }
  SCREAMING_SNAKE_CASE : str          =    sorted(arg_to_scheduler.keys())
  SCREAMING_SNAKE_CASE : List[str]          =    "{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  
  
  
  
  class 					snake_case__  ( pl.LightningModule       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=None				,    UpperCamelCase_="base"				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    **UpperCamelCase_				,    )		->			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(a_							)
  								a_       :   str			=	0
  								a_       :   List[Any]			=	Path(self.hparams.output_dir							)
  								a_       :   List[str]			=	self.hparams.cache_dir if self.hparams.cache_dir else None
  								if config is None:
  															a_       :   List[Any]			=	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=a_				,    **a_				,    )
  								else:
  															a_       :   List[Any]			=	config
  
  								a_       :   List[str]			=	("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  								for p in extra_model_params:
  															if getattr(self.hparams				,    a_				,    a_							):
  																						assert hasattr(self.config				,    a_							), f"""model config doesn't have a `{p}` attribute"""
  																						setattr(self.config				,    a_				,    getattr(self.hparams				,    a_							)							)
  
  								if tokenizer is None:
  															a_       :   Optional[Any]			=	AutoTokenizer.from_pretrained(
  															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,    cache_dir=a_				,    )
  								else:
  															a_       :   List[str]			=	tokenizer
  								a_       :   Optional[int]			=	MODEL_MODES[mode]
  								if model is None:
  															a_       :   Union[str, Any]			=	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=a_				,    )
  								else:
  															a_       :   int			=	model
  
  
  
  
  
  	def     A				(  self				,    *UpperCamelCase_				,    **UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.model_type.from_pretrained(*a_				,    **a_							)
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	arg_to_scheduler[self.hparams.lr_scheduler]
  								a_       :   Dict			=	get_schedule_func(
  								    self.opt				,    num_warmup_steps=self.hparams.warmup_steps				,    num_training_steps=self.total_steps()							)
  								a_       :   Dict			=	{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  								return scheduler
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	self.model
  								a_       :   List[str]			=	["""bias""", """LayerNorm.weight"""]
  								a_       :   Dict			=	[
  								    {
  								        """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:
  															a_       :   List[str]			=	Adafactor(
  															    a_				,    lr=self.hparams.learning_rate				,    scale_parameter=a_				,    relative_step=a_							)
  
  								else:
  															a_       :   int			=	AdamW(
  															    a_				,    lr=self.hparams.learning_rate				,    eps=self.hparams.adam_epsilon							)
  								a_       :   Optional[Any]			=	optimizer
  
  								a_       :   Optional[Any]			=	self.get_lr_scheduler()
  
  								return [optimizer], [scheduler]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.validation_step(a_				,    a_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.validation_end(a_							)
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	max(1				,    self.hparams.gpus							)  # TODO: consider num_tpu_cores
  								a_       :   Any			=	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				,    UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								if mode == "test":
  															a_       :   str			=	len(self.test_dataloader().dataset							)
  								else:
  															a_       :   Dict			=	self.get_dataloader("""train"""				,    self.hparams.train_batch_size				,    shuffle=a_							)
  															a_       :   Tuple			=	len(self.train_dataloader().dataset							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ = False							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								raise NotImplementedError("""You must implement this for your task"""							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.train_loader
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_dataloader("""dev"""				,    self.hparams.eval_batch_size				,    shuffle=a_							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_dataloader("""test"""				,    self.hparams.eval_batch_size				,    shuffle=a_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								return os.path.join(
  								    self.hparams.data_dir				,    """cached_{}_{}_{}""".format(
  								        a_				,    list(filter(a_				,    self.hparams.model_name_or_path.split("""/"""							)							)							).pop()				,    str(self.hparams.max_seq_length							)				,    )				,    )
  
  
  
  
  
  	@pl.utilities.rank_zero_only
  	def     A				(  self				,    UpperCamelCase_							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.output_dir.joinpath("""best_tfmr"""							)
  								a_       :   str			=	self.step_count
  								self.model.save_pretrained(a_							)
  								self.tokenizer.save_pretrained(a_							)
  
  
  
  
  
  	@staticmethod
  	def     A				(  UpperCamelCase_				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								parser.add_argument(
  								    """--model_name_or_path"""				,    default=a_				,    type=a_				,    required=a_				,    help="""Path to pretrained model or model identifier from huggingface.co/models"""				,    )
  								parser.add_argument(
  								    """--config_name"""				,    default=""""""				,    type=a_				,    help="""Pretrained config name or path if not the same as model_name"""							)
  								parser.add_argument(
  								    """--tokenizer_name"""				,    default=a_				,    type=a_				,    help="""Pretrained tokenizer name or path if not the same as model_name"""				,    )
  								parser.add_argument(
  								    """--cache_dir"""				,    default=""""""				,    type=a_				,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,    )
  								parser.add_argument(
  								    """--encoder_layerdrop"""				,    type=a_				,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,    )
  								parser.add_argument(
  								    """--decoder_layerdrop"""				,    type=a_				,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,    )
  								parser.add_argument(
  								    """--dropout"""				,    type=a_				,    help="""Dropout probability (Optional). Goes into model.config"""				,    )
  								parser.add_argument(
  								    """--attention_dropout"""				,    type=a_				,    help="""Attention dropout probability (Optional). Goes into model.config"""				,    )
  								parser.add_argument("""--learning_rate"""				,    default=5e-5				,    type=a_				,    help="""The initial learning rate for Adam."""							)
  								parser.add_argument(
  								    """--lr_scheduler"""				,    default="""linear"""				,    choices=a_				,    metavar=a_				,    type=a_				,    help="""Learning rate scheduler"""				,    )
  								parser.add_argument("""--weight_decay"""				,    default=0.0				,    type=a_				,    help="""Weight decay if we apply some."""							)
  								parser.add_argument("""--adam_epsilon"""				,    default=1e-8				,    type=a_				,    help="""Epsilon for Adam optimizer."""							)
  								parser.add_argument("""--warmup_steps"""				,    default=0				,    type=a_				,    help="""Linear warmup over warmup_steps."""							)
  								parser.add_argument("""--num_workers"""				,    default=4				,    type=a_				,    help="""kwarg passed to DataLoader"""							)
  								parser.add_argument("""--num_train_epochs"""				,    dest="""max_epochs"""				,    default=3				,    type=a_							)
  								parser.add_argument("""--train_batch_size"""				,    default=32				,    type=a_							)
  								parser.add_argument("""--eval_batch_size"""				,    default=32				,    type=a_							)
  								parser.add_argument("""--adafactor"""				,    action="""store_true"""							)
  
  
  
  
  
  class 					snake_case__  ( pl.Callback       ):
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	trainer.lr_schedulers[0]["""scheduler"""]
  								a_       :   List[str]			=	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
  								pl_module.logger.log_metrics(a_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								rank_zero_info("""***** Validation results *****"""							)
  								a_       :   Dict			=	trainer.callback_metrics
  								# Log results
  								for key in sorted(a_							):
  															if key not in ["log", "progress_bar"]:
  																						rank_zero_info("""{} = {}\n""".format(a_				,    str(metrics[key]							)							)							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								rank_zero_info("""***** Test results *****"""							)
  								a_       :   int			=	trainer.callback_metrics
  								# Log and save results to file
  								a_       :   Optional[Any]			=	os.path.join(pl_module.hparams.output_dir				,    """test_results.txt"""							)
  								with open(a_				,    """w"""							) as writer:
  															for key in sorted(a_							):
  																						if key not in ["log", "progress_bar"]:
  																													rank_zero_info("""{} = {}\n""".format(a_				,    str(metrics[key]							)							)							)
  																													writer.write("""{} = {}\n""".format(a_				,    str(metrics[key]							)							)							)
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Dict      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							parser.add_argument(
  							    """--output_dir"""	,	default=UpperCamelCase__	,	type=UpperCamelCase__	,	required=UpperCamelCase__	,	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=UpperCamelCase__	,	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=UpperCamelCase__      )
  							parser.add_argument("""--max_grad_norm"""	,	dest="""gradient_clip_val"""	,	default=1.0	,	type=UpperCamelCase__	,	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=UpperCamelCase__	,	default=1	,	help="""Number of updates steps to accumulate before performing a backward/update pass."""	,	)
  							parser.add_argument("""--seed"""	,	type=UpperCamelCase__	,	default=42	,	help="""random seed for initialization"""      )
  							parser.add_argument(
  							    """--data_dir"""	,	default=UpperCamelCase__	,	type=UpperCamelCase__	,	required=UpperCamelCase__	,	help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""	,	)
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: List[str]=True	,	SCREAMING_SNAKE_CASE_				: str=[]	,	SCREAMING_SNAKE_CASE_				: Dict=None	,	SCREAMING_SNAKE_CASE_				: List[str]=None	,	**SCREAMING_SNAKE_CASE_				: Optional[int]	,	):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							pl.seed_everything(args.seed      )
  
  							# init model
  							a_       :   Union[str, Any]			=	Path(model.hparams.output_dir      )
  							odir.mkdir(exist_ok=UpperCamelCase__      )
  
  							# add custom checkpoints
  							if checkpoint_callback is None:
  														a_       :   List[str]			=	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(UpperCamelCase__      )
  							if logging_callback is None:
  														a_       :   List[str]			=	LoggingCallback()
  
  							a_       :   Union[str, Any]			=	{}
  
  							# TODO: remove with PyTorch 1.6 since pl uses native amp
  							if args.fpaa:
  														a_       :   Optional[int]			=	16
  														a_       :   Tuple			=	args.fpaa_opt_level
  
  							if args.gpus > 1:
  														a_       :   Optional[int]			=	"""ddp"""
  
  							a_       :   Any			=	args.accumulate_grad_batches
  							a_       :   Any			=	extra_train_kwargs.get("""accelerator"""	,	UpperCamelCase__      )
  							a_       :   List[str]			=	extra_train_kwargs.get("""profiler"""	,	UpperCamelCase__      )
  
  							a_       :   Tuple			=	pl.Trainer.from_argparse_args(
  							    UpperCamelCase__	,	weights_summary=UpperCamelCase__	,	callbacks=[logging_callback] + extra_callbacks	,	logger=UpperCamelCase__	,	checkpoint_callback=UpperCamelCase__	,	**UpperCamelCase__	,	)
  
  							if args.do_train:
  														trainer.fit(UpperCamelCase__      )
  
  							return trainer
  
  
  
style_context: 
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
  							if n in (0, 1):
  														return 0
  							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  