class_docstr: 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.
class_docstr_tok: ["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","."]
code: def __post_init__(self):
      if self.task_name is not None:
          self.task_name = self.task_name.lower()
          if self.task_name not in task_to_keys.keys():
              raise ValueError(
                  "Unknown task, you should pick one in "
                  + ",".join(task_to_keys.keys())
              )
      elif self.dataset_name is not None:
          pass
      elif self.train_file is None or self.validation_file is None:
          raise ValueError(
              "Need either a GLUE task, a training/validation file or a dataset name."
          )
      else:
          train_extension = self.train_file.split(".")[-1]
          assert train_extension in [
              "csv",
              "json",
          ], "`train_file` should be a csv or a json file."
          validation_extension = self.validation_file.split(".")[-1]
          assert (
              validation_extension == train_extension
          ), "`validation_file` should have the same extension (csv or json) as `train_file`."
  
imports: import json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from typing import Optional
  import datasets
  import numpy
  import transformers
  from datasets import load_dataset
  from evaluate import load
  from onnxruntime.quantization import QuantFormat, QuantizationMode, QuantType
  from transformers import AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TrainingArguments
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
  from optimum.onnxruntime import ORTQuantizer
  from optimum.onnxruntime.configuration import AutoCalibrationConfig, QuantizationConfig
  from optimum.onnxruntime.model import ORTModel
  from optimum.onnxruntime.modeling_ort import ORTModelForSequenceClassification
  from optimum.onnxruntime.preprocessors import QuantizationPreprocessor
  from optimum.onnxruntime.preprocessors.passes import ExcludeGeLUNodes, ExcludeLayerNormNodes, ExcludeNodeAfter, ExcludeNodeFollowedBy
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