text: import logging
  import os
  import time
  from dataclasses import dataclass, field
  from enum import Enum
  from typing import List, Optional, Union
  
  import torch
  from filelock import FileLock
  from torch.utils.data.dataset import Dataset
  
  from ...tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
  from ...tokenization_utils import PreTrainedTokenizer
  from ...tokenization_xlm_roberta import XLMRobertaTokenizer
  from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
  from ..processors.utils import InputFeatures
  
  
  logger = logging.getLogger(__name__)
  
  
  @dataclass
  class GlueDataTrainingArguments:
      """
      Arguments pertaining to what data we are going to input our model for training and eval.
  
      Using `HfArgumentParser` we can turn this class
      into argparse arguments to be able to specify them on
      the command line.
      """
  
      task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
      data_dir: str = field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}
      )
      max_seq_length: int = field(
          default=128,
          metadata={
              "help": "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          },
      )
      overwrite_cache: bool = field(
          default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
      )
  
      def __post_init__(self):
          self.task_name = self.task_name.lower()
  
  
  class Split(Enum):
      train = "train"
      dev = "dev"
      test = "test"
  
  
  class GlueDataset(Dataset):
      """
      This will be superseded by a framework-agnostic approach
      soon.
      """
  
      args: GlueDataTrainingArguments
      output_mode: str
      features: List[InputFeatures]
  
      def __init__(
          self,
          args: GlueDataTrainingArguments,
          tokenizer: PreTrainedTokenizer,
          limit_length: Optional[int] = None,
          mode: Union[str, Split] = Split.train,
      ):
          self.args = args
          self.processor = glue_processors[args.task_name]()
          self.output_mode = glue_output_modes[args.task_name]
          if isinstance(mode, str):
              try:
                  mode = Split[mode]
              except KeyError:
                  raise KeyError("mode is not a valid split name")
          # Load data features from cache or dataset file
          cached_features_file = os.path.join(
              args.data_dir,
              "cached_{}_{}_{}_{}".format(
                  mode.value, tokenizer.__class__.__name__, str(args.max_seq_length), args.task_name,
              ),
          )
          label_list = self.processor.get_labels()
          if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
              RobertaTokenizer,
              RobertaTokenizerFast,
              XLMRobertaTokenizer,
          ):
              # HACK(label indices are swapped in RoBERTa pretrained model)
              label_list[1], label_list[2] = label_list[2], label_list[1]
          self.label_list = label_list
  
          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lock_path = cached_features_file + ".lock"
          with FileLock(lock_path):
  
              if os.path.exists(cached_features_file) and not args.overwrite_cache:
                  start = time.time()
                  self.features = torch.load(cached_features_file)
                  logger.info(
                      f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                  )
              else:
                  logger.info(f"Creating features from dataset file at {args.data_dir}")
  
                  if mode == Split.dev:
                      examples = self.processor.get_dev_examples(args.data_dir)
                  elif mode == Split.test:
                      examples = self.processor.get_test_examples(args.data_dir)
                  else:
                      examples = self.processor.get_train_examples(args.data_dir)
                  if limit_length is not None:
                      examples = examples[:limit_length]
                  self.features = glue_convert_examples_to_features(
                      examples,
                      tokenizer,
                      max_length=args.max_seq_length,
                      label_list=label_list,
                      output_mode=self.output_mode,
                  )
                  start = time.time()
                  torch.save(self.features, cached_features_file)
                  # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                  logger.info(
                      "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                  )
  
      def __len__(self):
          return len(self.features)
  
      def __getitem__(self, i) -> InputFeatures:
          return self.features[i]
  
      def get_labels(self):
          return self.label_list
  