code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a Masked Language Modeling 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(
          "--validation_file",
          type=str,
          default=None,
          help="A csv or a json file containing the validation data.",
      )
      parser.add_argument(
          "--validation_split_percentage",
          default=5,
          help="The percentage of the train set used as validation set in case there's no validation split",
      )
      parser.add_argument(
          "--pad_to_max_length",
          action="store_true",
          help="If passed, pad all samples to `max_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=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(
          "--max_seq_length",
          type=int,
          default=None,
          help=(
              "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated."
          ),
      )
      parser.add_argument(
          "--line_by_line",
          type=bool,
          default=False,
          help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
      )
      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(
          "--mlm_probability",
          type=float,
          default=0.15,
          help="Ratio of tokens to mask for masked language modeling loss",
      )
      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(
          "--trust_remote_code",
          type=bool,
          default=False,
          help=(
              "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
              "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
              "execute code present on the Hub on your local machine."
          ),
      )
      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"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
              "Only applicable when `--with_tracking` is passed."
          ),
      )
      parser.add_argument(
          "--low_cpu_mem_usage",
          action="store_true",
          help=(
              "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
              "If passed, LLM loading time and RAM consumption will be benefited."
          ),
      )
      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 dataset name or a training/validation file."
          )
      else:
          if args.train_file is not None:
              extension = args.train_file.split(".")[-1]
              if extension not in ["csv", "json", "txt"]:
                  raise ValueError(
                      "`train_file` should be a csv, json or txt file."
                  )
          if args.validation_file is not None:
              extension = args.validation_file.split(".")[-1]
              if extension not in ["csv", "json", "txt"]:
                  raise ValueError(
                      "`validation_file` should be a csv, json or txt file."
                  )
  
      if args.push_to_hub:
          if args.output_dir is None:
              raise ValueError(
                  "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 itertools import chain
  from pathlib import Path
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  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
  import transformers
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, SchedulerType, 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 1 for calling a global function with example usage: parse_args() and returns: args