class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
code: def __post_init__(self):
      if (
          self.dataset_name is None
          and self.train_file is None
          and self.validation_file is None
      ):
          raise ValueError(
              "Need either a dataset name or a training/validation file."
          )
      else:
          if self.train_file is not None:
              extension = self.train_file.split(".")[-1]
              if extension not in ["csv", "json", "txt"]:
                  raise ValueError(
                      "`train_file` should be a csv, a json or a txt file."
                  )
          if self.validation_file is not None:
              extension = self.validation_file.split(".")[-1]
              if extension not in ["csv", "json", "txt"]:
                  raise ValueError(
                      "`validation_file` should be a csv, a json or a txt file."
                  )
  
imports: import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional
  import datasets
  from datasets import load_dataset
  import adapters
  import evaluate
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, HfArgumentParser, Trainer, TrainingArguments, is_torch_tpu_available, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling the DataTrainingArguments obj's underlying member method code with example usage: obj.__post_init__() without return types