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 numpy
  import torch
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import evaluate
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import Repository
  from transformers import AdamW, DataCollatorWithPadding, EvalPrediction, SchedulerType, XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizerFast, default_data_collator, get_scheduler
  from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
  from transformers.utils.versions import require_version
  from utils_qa import postprocess_qa_predictions_with_beam_search
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args