code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Finetune a transformers model on a simple Masked Image Modeling task"
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default="cifar10",
          help="Name of a dataset from the datasets package",
      )
      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(
          "--image_column_name",
          type=str,
          default=None,
          help="The column name of the images in the files. If not set, will try to use 'image' or 'img'.",
      )
      parser.add_argument(
          "--train_dir",
          type=str,
          default=None,
          help="A folder containing the training data.",
      )
      parser.add_argument(
          "--validation_dir",
          type=None,
          default=None,
          help="A folder containing the validation data.",
      )
      parser.add_argument(
          "--train_val_split",
          type=float,
          default=0.15,
          help="Percent to split off of train for validation.",
      )
      parser.add_argument(
          "--mask_patch_size",
          type=int,
          default=32,
          help="The size of the square patches to use for masking.",
      )
      parser.add_argument(
          "--mask_ratio",
          type=float,
          default=0.6,
          help="Percentage of patches to mask.",
      )
      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(
          "--model_name_or_path",
          type=str,
          default=None,
          help=(
              "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
              "checkpoint identifier on the hub. "
              "Don't set if you want to train a model from scratch."
          ),
      )
      parser.add_argument(
          "--model_type",
          type=str,
          default=None,
          help="If training from scratch, pass a model type from the list: "
          + ", ".join(MODEL_TYPES),
      )
      parser.add_argument(
          "--config_name_or_path",
          type=str,
          default=None,
          help="Pretrained config name or path if not the same as model_name",
      )
      parser.add_argument(
          "--config_overrides",
          type=str,
          default=None,
          help=(
              "Override some existing default config settings when a model is trained from scratch. Example: "
              "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
          ),
      )
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="Where do you want to store (cache) the pretrained models/datasets downloaded from the hub",
      )
      parser.add_argument(
          "--model_revision",
          type=str,
          default="main",
          help="The specific model version to use (can be a branch name, tag name or commit id).",
      )
      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(
          "--image_processor_name",
          type=str,
          default=None,
          help="Name or path of preprocessor config.",
      )
      parser.add_argument(
          "--token",
          type=str,
          default=None,
          help=(
              "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
              "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
          ),
      )
      parser.add_argument(
          "--use_auth_token",
          type=bool,
          default=None,
          help="The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
      )
      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(
          "--image_size",
          type=int,
          default=None,
          help="The size (resolution) of each image. If not specified, will use `image_size` of the configuration.",
      )
      parser.add_argument(
          "--patch_size",
          type=int,
          default=None,
          help="The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.",
      )
      parser.add_argument(
          "--encoder_stride",
          type=int,
          default=None,
          help={"help": "Stride to use for the encoder."},
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      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(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--per_device_train_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-5,
          help="The initial learning rate for [`AdamW`] optimizer.",
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--num_train_epochs",
          type=float,
          default=3.0,
          help="Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).",
      )
      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(
          "--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(
          "--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(
          "--per_device_eval_batch_size",
          type=int,
          default=8,
          help="Batch size (per device) for the evaluation dataloader.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default=None,
          help="Where to store the final model.",
      )
      args = parser.parse_args()
  
      # Sanity checks
      data_files = {}
      if args.train_dir is not None:
          data_files["train"] = args.train_dir
      if args.validation_dir is not None:
          data_files["val"] = args.validation_dir
      args.data_files = data_files if data_files else None
  
      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 logging
  import math
  import os
  import warnings
  from pathlib import Path
  import datasets
  import numpy
  import torch
  from accelerate import Accelerator, DistributedType
  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 torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
  from tqdm.auto import tqdm
  import transformers
  from transformers import CONFIG_MAPPING, IMAGE_PROCESSOR_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, AutoConfig, AutoImageProcessor, AutoModelForMaskedImageModeling, 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