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