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(
          "--pretrained_vae_name_or_path",
          type=str,
          default=None,
          help="Path to pretrained vae or vae 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(
          "--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 there are not enough images already present in"
              " class_data_dir, additional images will be sampled with class_prompt."
          ),
      )
      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(
          "--save_steps",
          type=int,
          default=None,
          help="Save a checkpoint every X steps.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=0,
          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(
          "--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(
          "--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",
      )
  
      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 logging
  import math
  import os
  from pathlib import Path
  import jax
  import jax.numpy
  import numpy
  import optax
  import torch
  import torch.utils.checkpoint
  import transformers
  from flax import jax_utils
  from flax.training import train_state
  from flax.training.common_utils import shard
  from huggingface_hub import create_repo, upload_folder
  from huggingface_hub.utils import insecure_hashlib
  from jax.experimental.compilation_cache import compilation_cache
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
  from diffusers import FlaxAutoencoderKL, FlaxDDPMScheduler, FlaxPNDMScheduler, FlaxStableDiffusionPipeline, FlaxUNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
  from diffusers.utils import check_min_version
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args