code: def parse_args(input_args=None):
      parser = argparse.ArgumentParser(
          description="Simple example of a ControlNet 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(
          "--controlnet_model_name_or_path",
          type=str,
          default=None,
          help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
          " If not specified controlnet weights are initialized from unet.",
      )
      parser.add_argument(
          "--revision",
          type=str,
          default=None,
          required=False,
          help=(
              "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
              " float32 precision."
          ),
      )
      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(
          "--output_dir",
          type=str,
          default="controlnet-model",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="The directory where the downloaded models and datasets will be stored.",
      )
      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_batch_size",
          type=int,
          default=4,
          help="Batch size (per device) for the training dataloader.",
      )
      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. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
              "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
              "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
              "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
              "instructions."
          ),
      )
      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-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(
          "--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(
          "--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(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
      parser.add_argument(
          "--set_grads_to_none",
          action="store_true",
          help=(
              "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
              " behaviors, so disable this argument if it causes any problems. More info:"
              " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
          ),
      )
      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 🤗 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(
          "--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(
          "--image_column",
          type=str,
          default="image",
          help="The column of the dataset containing the target image.",
      )
      parser.add_argument(
          "--conditioning_image_column",
          type=str,
          default="conditioning_image",
          help="The column of the dataset containing the controlnet conditioning image.",
      )
      parser.add_argument(
          "--caption_column",
          type=str,
          default="text",
          help="The column of the dataset containing a caption or a list of captions.",
      )
      parser.add_argument(
          "--max_train_samples",
          type=int,
          default=None,
          help=(
              "For debugging purposes or quicker training, truncate the number of training examples to this "
              "value if set."
          ),
      )
      parser.add_argument(
          "--proportion_empty_prompts",
          type=float,
          default=0,
          help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
      )
      parser.add_argument(
          "--validation_prompt",
          type=str,
          default=None,
          nargs="+",
          help=(
              "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
              " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
              " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
          ),
      )
      parser.add_argument(
          "--validation_image",
          type=str,
          default=None,
          nargs="+",
          help=(
              "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`"
              " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
              " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
              " `--validation_image` that will be used with all `--validation_prompt`s."
          ),
      )
      parser.add_argument(
          "--num_validation_images",
          type=int,
          default=4,
          help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
      )
      parser.add_argument(
          "--validation_steps",
          type=int,
          default=100,
          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(
          "--tracker_project_name",
          type=str,
          default="train_controlnet",
          help=(
              "The `project_name` argument passed to Accelerator.init_trackers for"
              " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
          ),
      )
  
      if input_args is not None:
          args = parser.parse_args(input_args)
      else:
          args = parser.parse_args()
  
      if args.dataset_name is None and args.train_data_dir is None:
          raise ValueError(
              "Specify either `--dataset_name` or `--train_data_dir`"
          )
  
      if (
          args.dataset_name is not None
          and args.train_data_dir is not None
      ):
          raise ValueError(
              "Specify only one of `--dataset_name` or `--train_data_dir`"
          )
  
      if (
          args.proportion_empty_prompts < 0
          or args.proportion_empty_prompts > 1
      ):
          raise ValueError(
              "`--proportion_empty_prompts` must be in the range [0, 1]."
          )
  
      if (
          args.validation_prompt is not None
          and args.validation_image is None
      ):
          raise ValueError(
              "`--validation_image` must be set if `--validation_prompt` is set"
          )
  
      if (
          args.validation_prompt is None
          and args.validation_image is not None
      ):
          raise ValueError(
              "`--validation_prompt` must be set if `--validation_image` is set"
          )
  
      if (
          args.validation_image is not None
          and args.validation_prompt is not None
          and len(args.validation_image) != 1
          and len(args.validation_prompt) != 1
          and len(args.validation_image) != len(args.validation_prompt)
      ):
          raise ValueError(
              "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
              " or the same number of `--validation_prompt`s and `--validation_image`s"
          )
  
      if args.resolution % 8 != 0:
          raise ValueError(
              "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the controlnet encoder."
          )
  
      return args
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import accelerate
  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 datasets import load_dataset
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from PIL import Image
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
  import diffusers
  from diffusers import AutoencoderKL, ControlNetModel, DDPMScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 5 for calling a global function with example usage: parse_args(input_args) and returns: args