code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a text classification 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(
          "--predict_with_generate",
          type=bool,
          default=True,
          help="",
      )
      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(
          "--num_beams",
          type=int,
          default=None,
          help=(
              "Number of beams to use for evaluation. This argument will be "
              "passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."
          ),
      )
  
      parser.add_argument(
          "--max_source_length",
          type=int,
          default=1024,
          help=(
              "The maximum total input sequence length after "
              "tokenization.Sequences longer than this will be truncated, sequences shorter will be padded."
          ),
      )
      parser.add_argument(
          "--max_target_length",
          type=int,
          default=128,
          help=(
              "The maximum total sequence length for target text after "
              "tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
              "during ``evaluate`` and ``predict``."
          ),
      )
      parser.add_argument(
          "--val_max_target_length",
          type=int,
          default=None,
          help=(
              "The maximum total sequence length for validation "
              "target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
              "padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
              "param of ``model.generate``, which is used during ``evaluate`` and ``predict``."
          ),
      )
      parser.add_argument(
          "--pad_to_max_length",
          type=bool,
          default=False,
          help=(
              "Whether to pad all samples to model maximum sentence "
              "length. If False, will pad the samples dynamically when batching to the maximum length in the batch. More"
              "efficient on GPU but very bad for TPU."
          ),
      )
      parser.add_argument(
          "--validation_file",
          type=str,
          default=None,
          help="A csv or a json file containing the validation data.",
      )
      parser.add_argument(
          "--ignore_pad_token_for_loss",
          type=bool,
          default=True,
          help="Whether to ignore the tokens corresponding to padded labels in the loss computation or not.",
      )
      parser.add_argument(
          "--source_lang",
          type=str,
          default=None,
          help="Source language id for translation.",
      )
      parser.add_argument(
          "--target_lang",
          type=str,
          default=None,
          help="Target language id for translation.",
      )
      parser.add_argument(
          "--source_prefix",
          type=str,
          default=None,
          help="A prefix to add before every source text (useful for T5 models).",
      )
      parser.add_argument(
          "--preprocessing_num_workers",
          type=int,
          default=None,
          help="The number of processes to use for the preprocessing.",
      )
      parser.add_argument(
          "--overwrite_cache",
          action="store_true",
          help="Overwrite the cached training and evaluation sets",
      )
      parser.add_argument(
          "--max_length",
          type=int,
          default=128,
          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(
          "--model_name_or_path",
          type=str,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
          required=False,
      )
      parser.add_argument(
          "--config_name",
          type=str,
          default=None,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--tokenizer_name",
          type=str,
          default=None,
          help="Pretrained tokenizer name or path if not the same as model_name",
      )
      parser.add_argument(
          "--use_slow_tokenizer",
          action="store_true",
          help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
      )
      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(
          "--model_type",
          type=str,
          default=None,
          help="Model type to use if training from scratch.",
          choices=MODEL_TYPES,
      )
      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 enable experiment trackers for logging.",
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="all",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
              ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
              "Only applicable when `--with_tracking` is passed."
          ),
      )
      args = parser.parse_args()
  
      # Sanity checks
  
      if (
          args.dataset_name is None
          and args.train_file is None
          and args.validation_file is None
      ):
          raise ValueError(
              "Need either a task name or a training/validation file."
          )
  
      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.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 CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, MBartTokenizer, MBartTokenizerFast, SchedulerType, 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
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args