code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a Question Answering task"
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help="The name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The configuration name of the dataset to use (via the datasets library).",
      )
      parser.add_argument(
          "--train_file",
          type=str,
          default=None,
          help="A csv or a json file containing the training data.",
      )
      parser.add_argument(
          "--preprocessing_num_workers",
          type=int,
          default=1,
          help="A csv or a json file containing the training data.",
      )
      parser.add_argument(
          "--do_predict",
          action="store_true",
          help="Eval the question answering model",
      )
      parser.add_argument(
          "--validation_file",
          type=str,
          default=None,
          help="A csv or a json file containing the validation data.",
      )
      parser.add_argument(
          "--test_file",
          type=str,
          default=None,
          help="A csv or a json file containing the Prediction data.",
      )
      parser.add_argument(
          "--max_seq_length",
          type=int,
          default=384,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
              " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
          ),
      )
      parser.add_argument(
          "--pad_to_max_length",
          action="store_true",
          help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.",
      )
      parser.add_argument(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=True,
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--num_train_epochs",
          type=int,
          default=3,
          help="Total number of training epochs to perform.",
      )
      parser.add_argument(
          "--max_train_steps",
          type=int,
          default=None,
          help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
      )
      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(
          "--lr_scheduler_type",
          type=SchedulerType,
          default="linear",
          help="The scheduler type to use.",
          choices=[
              "linear",
              "cosine",
              "cosine_with_restarts",
              "polynomial",
              "constant",
              "constant_with_warmup",
          ],
      )
      parser.add_argument(
          "--num_warmup_steps",
          type=int,
          default=0,
          help="Number of steps for the warmup in the lr scheduler.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          help="Where to store the final model.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--doc_stride",
          type=int,
          default=128,
          help="When splitting up a long document into chunks how much stride to take between chunks.",
      )
      parser.add_argument(
          "--n_best_size",
          type=int,
          default=20,
          help="The total number of n-best predictions to generate when looking for an answer.",
      )
      parser.add_argument(
          "--null_score_diff_threshold",
          type=float,
          default=0.0,
          help=(
              "The threshold used to select the null answer: if the best answer has a score that is less than "
              "the score of the null answer minus this threshold, the null answer is selected for this example. "
              "Only useful when `version_2_with_negative=True`."
          ),
      )
      parser.add_argument(
          "--version_2_with_negative",
          action="store_true",
          help="If true, some of the examples do not have an answer.",
      )
      parser.add_argument(
          "--max_answer_length",
          type=int,
          default=30,
          help=(
              "The maximum length of an answer that can be generated. This is needed because the start "
              "and end predictions are not conditioned on one another."
          ),
      )
      parser.add_argument(
          "--max_train_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--max_eval_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument(
          "--max_predict_samples",
          type=int,
          default=None,
          help="For debugging purposes or quicker training, truncate the number of prediction examples to this",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=str,
          default=None,
          help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help="If the training should continue from a checkpoint folder.",
      )
      parser.add_argument(
          "--with_tracking",
          action="store_true",
          help="Whether to load in all available experiment trackers from the environment and use them for logging.",
      )
      args = parser.parse_args()
  
      # Sanity checks
      if (
          args.dataset_name is None
          and args.train_file is None
          and args.validation_file is None
          and args.test_file is None
      ):
          raise ValueError(
              "Need either a dataset name or a training/validation/test file."
          )
      else:
          if args.train_file is not None:
              extension = args.train_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`train_file` should be a csv or a json file."
          if args.validation_file is not None:
              extension = args.validation_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`validation_file` should be a csv or a json file."
          if args.test_file is not None:
              extension = args.test_file.split(".")[-1]
              assert extension in [
                  "csv",
                  "json",
              ], "`test_file` should be a csv or a json file."
  
      if args.push_to_hub:
          assert (
              args.output_dir is not None
          ), "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
  
      return args
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import datasets
  import evaluate
  import numpy
  import torch
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from datasets import load_dataset
  from huggingface_hub import Repository, create_repo
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from utils_qa import postprocess_qa_predictions_with_beam_search
  import transformers
  from transformers import AdamW, DataCollatorWithPadding, EvalPrediction, SchedulerType, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizerFast, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args