code: def parse_args(input_args=None):
      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(
          "--revision",
          type=str,
          default=None,
          required=False,
          help="Revision of pretrained 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(
          "--train_data_dir",
          type=str,
          default=None,
          required=True,
          help="A folder containing the training data of images.",
      )
      parser.add_argument(
          "--num_validation_images",
          type=int,
          default=4,
          help="Number of images that should be generated during validation with `validation_conditioning`.",
      )
      parser.add_argument(
          "--validation_steps",
          type=int,
          default=100,
          help=(
              "Run realfill validation every X steps. RealFill validation consists of running the conditioning"
              " `args.validation_conditioning` multiple times: `args.num_validation_images`."
          ),
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="realfill-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(
          "--train_batch_size",
          type=int,
          default=4,
          help="Batch size (per device) for the training dataloader.",
      )
      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(
          "--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 also 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."),
      )
      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(
          "--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(
          "--unet_learning_rate",
          type=float,
          default=2e-4,
          help="Learning rate to use for unet.",
      )
      parser.add_argument(
          "--text_encoder_learning_rate",
          type=float,
          default=4e-5,
          help="Learning rate to use for text encoder.",
      )
      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(
          "--lr_num_cycles",
          type=int,
          default=1,
          help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
      )
      parser.add_argument(
          "--lr_power",
          type=float,
          default=1.0,
          help="Power factor of the polynomial 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(
          "--allow_tf32",
          action="store_true",
          help=(
              "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
              " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
          ),
      )
      parser.add_argument(
          "--report_to",
          type=str,
          default="tensorboard",
          help=(
              'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
              ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
          ),
      )
      parser.add_argument(
          "--wandb_key",
          type=str,
          default=None,
          help=(
              "If report to option is set to wandb, api-key for wandb used for login to wandb "
          ),
      )
      parser.add_argument(
          "--wandb_project_name",
          type=str,
          default=None,
          help=(
              "If report to option is set to wandb, project name in wandb for log tracking  "
          ),
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default=None,
          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.  Default to the value of accelerate config of the current system or the"
              " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
          ),
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      parser.add_argument(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
      parser.add_argument(
          "--set_grads_to_none",
          action="store_true",
          help=(
              "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
              " behaviors, so disable this argument if it causes any problems. More info:"
              " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
          ),
      )
      parser.add_argument(
          "--lora_rank",
          type=int,
          default=16,
          help=("The dimension of the LoRA update matrices."),
      )
      parser.add_argument(
          "--lora_alpha",
          type=int,
          default=27,
          help=("The alpha constant of the LoRA update matrices."),
      )
      parser.add_argument(
          "--lora_dropout",
          type=float,
          default=0.0,
          help="The dropout rate of the LoRA update matrices.",
      )
      parser.add_argument(
          "--lora_bias",
          type=str,
          default="none",
          help="The bias type of the Lora update matrices. Must be 'none', 'all' or 'lora_only'.",
      )
  
      if input_args is not None:
          args = parser.parse_args(input_args)
      else:
          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
  
      return args
  
imports: import argparse
  import copy
  import itertools
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import torchvision.transforms.v2
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from peft import LoraConfig, PeftModel, get_peft_model
  from PIL import Image
  from PIL.ImageOps import exif_transpose
  from torch.utils.data import Dataset
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, CLIPTextModel
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 4 for calling a global function with example usage: parse_args(input_args) and returns: args