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