code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of a training script."
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help=(
              "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
              " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
              " or to a folder containing files that HF Datasets can understand."
          ),
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The config of the Dataset, leave as None if there's only one config.",
      )
      parser.add_argument(
          "--model_config_name_or_path",
          type=str,
          default=None,
          help="The config of the UNet model to train, leave as None to use standard DDPM configuration.",
      )
      parser.add_argument(
          "--train_data_dir",
          type=str,
          default=None,
          help=(
              "A folder containing the training data. Folder contents must follow the structure described in"
              " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
              " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
          ),
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="ddpm-model-64",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument("--overwrite_output_dir", action="store_true")
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="The directory where the downloaded models and datasets will be stored.",
      )
      parser.add_argument(
          "--resolution",
          type=int,
          default=64,
          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(
          "--random_flip",
          default=False,
          action="store_true",
          help="whether to randomly flip images horizontally",
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=16,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--eval_batch_size",
          type=int,
          default=16,
          help="The number of images to generate for evaluation.",
      )
      parser.add_argument(
          "--dataloader_num_workers",
          type=int,
          default=0,
          help=(
              "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
              " process."
          ),
      )
      parser.add_argument("--num_epochs", type=int, default=100)
      parser.add_argument(
          "--save_images_epochs",
          type=int,
          default=10,
          help="How often to save images during training.",
      )
      parser.add_argument(
          "--save_model_epochs",
          type=int,
          default=10,
          help="How often to save the model during training.",
      )
      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=1e-4,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--lr_scheduler",
          type=str,
          default="cosine",
          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=0.95,
          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-6,
          help="Weight decay magnitude for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-08,
          help="Epsilon value for the Adam optimizer.",
      )
      parser.add_argument(
          "--use_ema",
          action="store_true",
          help="Whether to use Exponential Moving Average for the final model weights.",
      )
      parser.add_argument(
          "--ema_inv_gamma",
          type=float,
          default=1.0,
          help="The inverse gamma value for the EMA decay.",
      )
      parser.add_argument(
          "--ema_power",
          type=float,
          default=3 / 4,
          help="The power value for the EMA decay.",
      )
      parser.add_argument(
          "--ema_max_decay",
          type=float,
          default=0.9999,
          help="The maximum decay magnitude for EMA.",
      )
      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(
          "--hub_private_repo",
          action="store_true",
          help="Whether or not to create a private repository.",
      )
      parser.add_argument(
          "--logger",
          type=str,
          default="tensorboard",
          choices=["tensorboard", "wandb"],
          help=(
              "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
              " for experiment tracking and logging of model metrics and model checkpoints"
          ),
      )
      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(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      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(
          "--prediction_type",
          type=str,
          default="epsilon",
          choices=["epsilon", "sample"],
          help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
      )
      parser.add_argument("--ddpm_num_steps", type=int, default=1000)
      parser.add_argument(
          "--ddpm_num_inference_steps", type=int, default=1000
      )
      parser.add_argument(
          "--ddpm_beta_schedule", type=str, default="linear"
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=int,
          default=500,
          help=(
              "Save a checkpoint of the training state every X updates. These checkpoints are only 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(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
  
      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.dataset_name is None and args.train_data_dir is None:
          raise ValueError(
              "You must specify either a dataset name from the hub or a train data directory."
          )
  
      return args
  
imports: import argparse
  import inspect
  import logging
  import math
  import os
  import shutil
  from datetime import timedelta
  from pathlib import Path
  from typing import Optional
  import accelerate
  import datasets
  import torch
  import torch.nn.functional
  from accelerate import Accelerator, InitProcessGroupKwargs
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration
  from datasets import load_dataset
  from huggingface_hub import HfFolder, Repository, create_repo, whoami
  from packaging import version
  from torchvision import transforms
  from tqdm.auto import tqdm
  import diffusers
  from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import EMAModel
  from diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args