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(
          "--instance_data_dir",
          nargs="+",
          help="Instance data directories",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="text-inversion-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_text_encoder",
          default=False,
          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(
          "--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(
          "--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(
          "--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(
          "--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(
          "--checkpointing_steps",
          type=int,
          default=1000,
          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(
          "--checkpointing_from",
          type=int,
          default=1000,
          help=("Start to checkpoint from step"),
      )
      parser.add_argument(
          "--validation_steps",
          type=int,
          default=50,
          help=(
              "Run validation every X steps. Validation consists of running the prompt"
              " `args.validation_prompt` multiple times: `args.num_validation_images`"
              " and logging the images."
          ),
      )
      parser.add_argument(
          "--validation_from",
          type=int,
          default=0,
          help=("Start to validate from step"),
      )
      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(
          "--validation_project_name",
          type=str,
          default=None,
          help="The w&b name.",
      )
      parser.add_argument(
          "--report_to_wandb",
          default=False,
          action="store_true",
          help="Whether to report to weights and biases",
      )
  
      args = parser.parse_args()
  
      return args
  
imports: import argparse
  import copy
  import itertools
  import logging
  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 datasets import concatenate_datasets, load_dataset
  from PIL import Image
  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.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args