code: import logging
  import os
  import random
  
  import numpy as np
  import pytorch_lightning as pl
  import torch
  
  from transformers import (
      ALL_PRETRAINED_MODEL_ARCHIVE_MAP,
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      get_linear_schedule_with_warmup,
  )
  from transformers.modeling_auto import MODEL_MAPPING
  
  
  logger = logging.getLogger(__name__)
  
  
  ALL_MODELS = tuple(ALL_PRETRAINED_MODEL_ARCHIVE_MAP)
  MODEL_CLASSES = tuple(m.model_type for m in MODEL_MAPPING)
  
  MODEL_MODES = {
      "base": AutoModel,
      "sequence-classification": AutoModelForSequenceClassification,
      "question-answering": AutoModelForQuestionAnswering,
      "pretraining": AutoModelForPreTraining,
      "token-classification": AutoModelForTokenClassification,
      "language-modeling": AutoModelWithLMHead,
  }
  
  
  def set_seed(args):
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
  
  class BaseTransformer(pl.LightningModule):
      def __init__(self, hparams, num_labels=None, mode="base"):
          "Initialize a model."
  
          super(BaseTransformer, self).__init__()
          self.hparams = hparams
          self.hparams.model_type = self.hparams.model_type.lower()
          config = 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=self.hparams.cache_dir if self.hparams.cache_dir else None,
          )
          tokenizer = AutoTokenizer.from_pretrained(
              self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
              do_lower_case=self.hparams.do_lower_case,
              cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,
          )
          model = MODEL_MODES[mode].from_pretrained(
              self.hparams.model_name_or_path,
              from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
              config=config,
              cache_dir=self.hparams.cache_dir if self.hparams.cache_dir else None,
          )
          self.config, self.tokenizer, self.model = config, tokenizer, model
  
      def is_logger(self):
          return self.trainer.proc_rank <= 0
  
      def configure_optimizers(self):
          "Prepare optimizer and schedule (linear warmup and decay)"
  
          model = self.model
          no_decay = ["bias", "LayerNorm.weight"]
          optimizer_grouped_parameters = [
              {
                  "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,
              },
          ]
          optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
          self.opt = optimizer
          return [optimizer]
  
      def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
          if self.trainer.use_tpu:
              xm.optimizer_step(optimizer)
          else:
              optimizer.step()
          optimizer.zero_grad()
          self.lr_scheduler.step()
  
      def get_tqdm_dict(self):
          tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]}
  
          return tqdm_dict
  
      def test_step(self, batch, batch_nb):
          return self.validation_step(batch, batch_nb)
  
      def test_end(self, outputs):
          return self.validation_end(outputs)
  
      def train_dataloader(self):
          train_batch_size = self.hparams.train_batch_size
          dataloader = self.load_dataset("train", train_batch_size)
  
          t_total = (
              (len(dataloader.dataset) // (train_batch_size * max(1, self.hparams.n_gpu)))
              // self.hparams.gradient_accumulation_steps
              * float(self.hparams.num_train_epochs)
          )
          scheduler = get_linear_schedule_with_warmup(
              self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
          )
          self.lr_scheduler = scheduler
          return dataloader
  
      def val_dataloader(self):
          return self.load_dataset("dev", self.hparams.eval_batch_size)
  
      def test_dataloader(self):
          return self.load_dataset("test", self.hparams.eval_batch_size)
  
      def _feature_file(self, mode):
          return os.path.join(
              self.hparams.data_dir,
              "cached_{}_{}_{}".format(
                  mode,
                  list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
                  str(self.hparams.max_seq_length),
              ),
          )
  
      @staticmethod
      def add_model_specific_args(parser, root_dir):
          parser.add_argument(
              "--model_type",
              default=None,
              type=str,
              required=True,
              help="Model type selected in the list: " + ", ".join(MODEL_CLASSES),
          )
          parser.add_argument(
              "--model_name_or_path",
              default=None,
              type=str,
              required=True,
              help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
          )
          parser.add_argument(
              "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
          )
          parser.add_argument(
              "--tokenizer_name",
              default="",
              type=str,
              help="Pretrained tokenizer name or path if not the same as model_name",
          )
          parser.add_argument(
              "--cache_dir",
              default="",
              type=str,
              help="Where do you want to store the pre-trained models downloaded from s3",
          )
          parser.add_argument(
              "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
          )
          parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
          parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
          parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
          parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
          parser.add_argument(
              "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform."
          )
  
          parser.add_argument("--train_batch_size", default=32, type=int)
          parser.add_argument("--eval_batch_size", default=32, type=int)
  
  
  class LoggingCallback(pl.Callback):
      def on_validation_end(self, trainer, pl_module):
          logger.info("***** Validation results *****")
          if pl_module.is_logger():
              metrics = trainer.callback_metrics
              # Log results
              for key in sorted(metrics):
                  if key not in ["log", "progress_bar"]:
                      logger.info("{} = {}\n".format(key, str(metrics[key])))
  
      def on_test_end(self, trainer, pl_module):
          logger.info("***** Test results *****")
  
          if pl_module.is_logger():
              metrics = trainer.callback_metrics
  
              # Log and save results to file
              output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
              with open(output_test_results_file, "w") as writer:
                  for key in sorted(metrics):
                      if key not in ["log", "progress_bar"]:
                          logger.info("{} = {}\n".format(key, str(metrics[key])))
                          writer.write("{} = {}\n".format(key, str(metrics[key])))
  
  
  def add_generic_args(parser, root_dir):
      parser.add_argument(
          "--output_dir",
          default=None,
          type=str,
          required=True,
          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=str,
          default="O1",
          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_gpu", type=int, default=1)
      parser.add_argument("--n_tpu_cores", type=int, default=0)
      parser.add_argument("--max_grad_norm", default=1.0, type=float, 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",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
  
      parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.")
      parser.add_argument("--server_port", type=str, default="", help="For distant debugging.")
      parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
  
  
  def generic_train(model, args):
      # init model
      set_seed(args)
  
      # Setup distant debugging if needed
      if args.server_ip and args.server_port:
          # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
          import ptvsd
  
          print("Waiting for debugger attach")
          ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
          ptvsd.wait_for_attach()
  
      if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
          raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
  
      checkpoint_callback = pl.callbacks.ModelCheckpoint(
          filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=5
      )
  
      train_params = dict(
          accumulate_grad_batches=args.gradient_accumulation_steps,
          gpus=args.n_gpu,
          max_epochs=args.num_train_epochs,
          early_stop_callback=False,
          gradient_clip_val=args.max_grad_norm,
          checkpoint_callback=checkpoint_callback,
          callbacks=[LoggingCallback()],
      )
  
      if args.fp16:
          train_params["use_amp"] = args.fp16
          train_params["amp_level"] = args.fp16_opt_level
  
      if args.n_tpu_cores > 0:
          global xm
          import torch_xla.core.xla_model as xm
  
          train_params["num_tpu_cores"] = args.n_tpu_cores
          train_params["gpus"] = 0
  
      if args.n_gpu > 1:
          train_params["distributed_backend"] = "ddp"
  
      trainer = pl.Trainer(**train_params)
  
      if args.do_train:
          trainer.fit(model)
  
      return trainer
  