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(
          "--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(
          "--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(
          "--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_steps",
          type=int,
          default=100,
          help=(
              "Run dreambooth validation every X steps. Dreambooth validation consists of running the prompt"
              " `args.validation_prompt` multiple times: `args.num_validation_images`."
          ),
      )
      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(
          "--center_crop",
          action="store_true",
          help="Whether to center crop images before resizing to resolution",
      )
      parser.add_argument(
          "--train_text_encoder",
          action="store_true",
          help="Whether to train the text encoder",
      )
  
      # lora args
      parser.add_argument(
          "--use_lora",
          action="store_true",
          help="Whether to use Lora for parameter efficient tuning",
      )
      parser.add_argument(
          "--lora_r",
          type=int,
          default=8,
          help="Lora rank, only used if use_lora is True",
      )
      parser.add_argument(
          "--lora_alpha",
          type=int,
          default=32,
          help="Lora alpha, only used if use_lora is True",
      )
      parser.add_argument(
          "--lora_dropout",
          type=float,
          default=0.0,
          help="Lora dropout, only used if use_lora is True",
      )
      parser.add_argument(
          "--lora_bias",
          type=str,
          default="none",
          help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora is True",
      )
      parser.add_argument(
          "--lora_text_encoder_r",
          type=int,
          default=8,
          help="Lora rank for text encoder, only used if `use_lora` and `train_text_encoder` are True",
      )
      parser.add_argument(
          "--lora_text_encoder_alpha",
          type=int,
          default=32,
          help="Lora alpha for text encoder, only used if `use_lora` and `train_text_encoder` are True",
      )
      parser.add_argument(
          "--lora_text_encoder_dropout",
          type=float,
          default=0.0,
          help="Lora dropout for text encoder, only used if `use_lora` and `train_text_encoder` are True",
      )
      parser.add_argument(
          "--lora_text_encoder_bias",
          type=str,
          default="none",
          help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True",
      )
  
      parser.add_argument(
          "--num_dataloader_workers",
          type=int,
          default=1,
          help="Num of workers for the training dataloader.",
      )
  
      parser.add_argument(
          "--no_tracemalloc",
          default=False,
          action="store_true",
          help="Flag to stop memory allocation tracing during training. This could speed up training on Windows.",
      )
  
      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(
          "--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-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(
          "--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(
          "--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.",
      )
  
      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."
              )
  
      return args
  
imports: import argparse
  import gc
  import hashlib
  import itertools
  import logging
  import math
  import os
  import threading
  import warnings
  from contextlib import nullcontext
  from pathlib import Path
  from typing import Optional
  import datasets
  import diffusers
  import numpy
  import psutil
  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 set_seed
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version
  from diffusers.utils.import_utils import is_xformers_available
  from huggingface_hub import HfFolder, Repository, whoami
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  from peft import LoraConfig, get_peft_model
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args(input_args) and returns: args