class_docstr: Arguments pertaining to what data we are going to input our model for training and eval.
class_docstr_tok: ["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]
              assert extension in [
                  "csv",
                  "json",
                  "txt",
              ], "`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]
              assert extension in [
                  "csv",
                  "json",
                  "txt",
              ], "`validation_file` should be a csv, a json or a txt file."
  
imports: import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  import datasets
  import flax
  import jax
  import jax.numpy
  import numpy
  import optax
  from datasets import load_dataset
  from flax import jax_utils, traverse_util
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard
  from tqdm import tqdm
  from transformers import CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed
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