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(
          "--variant",
          type=str,
          default=None,
          help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
      )
      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,
          required=True,
          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(
          "--validation_prompt",
          type=str,
          default=None,
          help="A prompt that is used during validation to verify that the model is learning.",
      )
      parser.add_argument(
          "--num_validation_images",
          type=int,
          default=4,
          help="Number of images that should be generated during validation with `validation_prompt`.",
      )
      parser.add_argument(
          "--validation_epochs",
          type=int,
          default=50,
          help=(
              "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
              " `args.validation_prompt` multiple times: `args.num_validation_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="lora-dreambooth-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. If set, the text encoder should be float32 precision.",
      )
      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(
          "--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(
          "--learning_rate",
          type=float,
          default=5e-4,
          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(
          "--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(
          "--dataloader_num_workers",
          type=int,
          default=0,
          help=(
              "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
          ),
      )
      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(
          "--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(
          "--prior_generation_precision",
          type=str,
          default=None,
          choices=["no", "fp32", "fp16", "bf16"],
          help=(
              "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
              " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
          ),
      )
      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(
          "--pre_compute_text_embeddings",
          action="store_true",
          help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
      )
      parser.add_argument(
          "--tokenizer_max_length",
          type=int,
          default=None,
          required=False,
          help="The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.",
      )
      parser.add_argument(
          "--text_encoder_use_attention_mask",
          action="store_true",
          required=False,
          help="Whether to use attention mask for the text encoder",
      )
      parser.add_argument(
          "--validation_images",
          required=False,
          default=None,
          nargs="+",
          help="Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.",
      )
      parser.add_argument(
          "--class_labels_conditioning",
          required=False,
          default=None,
          help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
      )
      parser.add_argument(
          "--rank",
          type=int,
          default=4,
          help=("The dimension of the LoRA update matrices."),
      )
  
      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
  
      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."
              )
      else:
          # logger is not available yet
          if args.class_data_dir is not None:
              warnings.warn(
                  "You need not use --class_data_dir without --with_prior_preservation."
              )
          if args.class_prompt is not None:
              warnings.warn(
                  "You need not use --class_prompt without --with_prior_preservation."
              )
  
      if args.train_text_encoder and args.pre_compute_text_embeddings:
          raise ValueError(
              "`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`"
          )
  
      return args
  
imports: import argparse
  import copy
  import gc
  import logging
  import math
  import os
  import shutil
  import warnings
  from pathlib import Path
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  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 packaging import version
  from peft import LoraConfig
  from peft.utils import get_peft_model_state_dict, set_peft_model_state_dict
  from PIL import Image
  from PIL.ImageOps import exif_transpose
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import _set_state_dict_into_text_encoder, cast_training_params
  from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.torch_utils import is_compiled_module
label_desc: Use image node_id 3 for calling a global function with example usage: parse_args(input_args) and returns: args