code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of a training script."
      )
      parser.add_argument(
          "--pretrained_model_name_or_path",
          type=str,
          default=None,
          required=True,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      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(
          "--instance_data_dir",
          type=str,
          default=None,
          required=True,
          help="A folder containing the training data of instance images.",
      )
      parser.add_argument(
          "--class_data_dir",
          type=str,
          default=None,
          required=False,
          help="A folder containing the training data of class images.",
      )
      parser.add_argument(
          "--instance_prompt",
          type=str,
          default=None,
          help="The prompt with identifier specifying the instance",
      )
      parser.add_argument(
          "--class_prompt",
          type=str,
          default=None,
          help="The prompt to specify images in the same class as provided instance images.",
      )
      parser.add_argument(
          "--with_prior_preservation",
          default=False,
          action="store_true",
          help="Flag to add prior preservation loss.",
      )
      parser.add_argument(
          "--prior_loss_weight",
          type=float,
          default=1.0,
          help="The weight of prior preservation loss.",
      )
      parser.add_argument(
          "--num_class_images",
          type=int,
          default=100,
          help=(
              "Minimal class images for prior preservation loss. If not have enough images, additional images will be"
              " sampled with class_prompt."
          ),
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="dreambooth-inpaint-model",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=None,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--resolution",
          type=int,
          default=512,
          help=(
              "The resolution for input images, all the images in the train/validation dataset will be resized to this"
              " resolution"
          ),
      )
      parser.add_argument(
          "--center_crop",
          default=False,
          action="store_true",
          help=(
              "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
              " cropped. The images will be resized to the resolution first before cropping."
          ),
      )
      parser.add_argument(
          "--train_text_encoder",
          action="store_true",
          help="Whether to train the text encoder",
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=4,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--sample_batch_size",
          type=int,
          default=4,
          help="Batch size (per device) for sampling images.",
      )
      parser.add_argument("--num_train_epochs", type=int, default=1)
      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(
          "--gradient_checkpointing",
          action="store_true",
          help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=5e-6,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--scale_lr",
          action="store_true",
          default=False,
          help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
      )
      parser.add_argument(
          "--lr_scheduler",
          type=str,
          default="constant",
          help=(
              'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
              ' "constant", "constant_with_warmup"]'
          ),
      )
      parser.add_argument(
          "--lr_warmup_steps",
          type=int,
          default=500,
          help="Number of steps for the warmup in the lr scheduler.",
      )
      parser.add_argument(
          "--use_8bit_adam",
          action="store_true",
          help="Whether or not to use 8-bit Adam from bitsandbytes.",
      )
      parser.add_argument(
          "--adam_beta1",
          type=float,
          default=0.9,
          help="The beta1 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_beta2",
          type=float,
          default=0.999,
          help="The beta2 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_weight_decay",
          type=float,
          default=1e-2,
          help="Weight decay to use.",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-08,
          help="Epsilon value for the Adam optimizer",
      )
      parser.add_argument(
          "--max_grad_norm",
          default=1.0,
          type=float,
          help="Max gradient norm.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          default=None,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          default=None,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--logging_dir",
          type=str,
          default="logs",
          help=(
              "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
          ),
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default="no",
          choices=["no", "fp16", "bf16"],
          help=(
              "Whether to use mixed precision. Choose"
              "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
              "and an Nvidia Ampere GPU."
          ),
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=int,
          default=500,
          help=(
              "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
              " checkpoints in case they are better than the last checkpoint and are suitable for resuming training"
              " using `--resume_from_checkpoint`."
          ),
      )
      parser.add_argument(
          "--checkpoints_total_limit",
          type=int,
          default=None,
          help=(
              "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
              " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
              " for more docs"
          ),
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help=(
              "Whether training should be resumed from a previous checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
          ),
      )
      parser.add_argument(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
  
      args = parser.parse_args()
      env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
      if env_local_rank != -1 and env_local_rank != args.local_rank:
          args.local_rank = env_local_rank
  
      if args.instance_data_dir is None:
          raise ValueError("You must specify a train data directory.")
  
      if args.with_prior_preservation:
          if args.class_data_dir is None:
              raise ValueError(
                  "You must specify a data directory for class images."
              )
          if args.class_prompt is None:
              raise ValueError(
                  "You must specify prompt for class images."
              )
  
      return args
  
imports: import argparse
  import math
  import os
  import random
  from pathlib import Path
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from huggingface_hub import create_repo, upload_folder
  from huggingface_hub.utils import insecure_hashlib
  from PIL import Image, ImageDraw
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
  from diffusers.loaders import AttnProcsLayers
  from diffusers.models.attention_processor import LoRAAttnProcessor
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 3 for calling a global function with example usage: parse_args() and returns: args