code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of a training script."
      )
      # ----------Model Checkpoint Loading Arguments----------
      parser.add_argument(
          "--pretrained_teacher_model",
          type=str,
          default=None,
          required=True,
          help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--pretrained_vae_model_name_or_path",
          type=str,
          default=None,
          help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
      )
      parser.add_argument(
          "--teacher_revision",
          type=str,
          default=None,
          required=False,
          help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--revision",
          type=str,
          default=None,
          required=False,
          help="Revision of pretrained LDM model identifier from huggingface.co/models.",
      )
      # ----------Training Arguments----------
      # ----General Training Arguments----
      parser.add_argument(
          "--output_dir",
          type=str,
          default="lcm-xl-distilled",
          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.",
      )
      # ----Logging----
      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(
          "--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.'
          ),
      )
      # ----Checkpointing----
      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.'
          ),
      )
      # ----Image Processing----
      parser.add_argument(
          "--train_shards_path_or_url",
          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(
          "--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(
          "--interpolation_type",
          type=str,
          default="bilinear",
          help=(
              "The interpolation function used when resizing images to the desired resolution. Choose between `bilinear`,"
              " `bicubic`, `box`, `nearest`, `nearest_exact`, `hamming`, and `lanczos`."
          ),
      )
      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",
          action="store_true",
          help="whether to randomly flip images horizontally",
      )
      # ----Dataloader----
      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."
          ),
      )
      # ----Batch Size and Training Steps----
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=16,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument("--num_train_epochs", type=int, default=100)
      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(
          "--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."
          ),
      )
      # ----Learning Rate----
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=1e-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(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      # ----Optimizer (Adam)----
      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.",
      )
      # ----Diffusion Training Arguments----
      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).",
      )
      # ----Latent Consistency Distillation (LCD) Specific Arguments----
      parser.add_argument(
          "--w_min",
          type=float,
          default=5.0,
          required=False,
          help=(
              "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
              " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
              " compared to the original paper."
          ),
      )
      parser.add_argument(
          "--w_max",
          type=float,
          default=15.0,
          required=False,
          help=(
              "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
              " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
              " compared to the original paper."
          ),
      )
      parser.add_argument(
          "--num_ddim_timesteps",
          type=int,
          default=50,
          help="The number of timesteps to use for DDIM sampling.",
      )
      parser.add_argument(
          "--loss_type",
          type=str,
          default="l2",
          choices=["l2", "huber"],
          help="The type of loss to use for the LCD loss.",
      )
      parser.add_argument(
          "--huber_c",
          type=float,
          default=0.001,
          help="The huber loss parameter. Only used if `--loss_type=huber`.",
      )
      parser.add_argument(
          "--lora_rank",
          type=int,
          default=64,
          help="The rank of the LoRA projection matrix.",
      )
      parser.add_argument(
          "--lora_alpha",
          type=int,
          default=64,
          help=(
              "The value of the LoRA alpha parameter, which controls the scaling factor in front of the LoRA weight"
              " update delta_W. No scaling will be performed if this value is equal to `lora_rank`."
          ),
      )
      parser.add_argument(
          "--lora_dropout",
          type=float,
          default=0.0,
          help="The dropout probability for the dropout layer added before applying the LoRA to each layer input.",
      )
      parser.add_argument(
          "--lora_target_modules",
          type=str,
          default=None,
          help=(
              "A comma-separated string of target module keys to add LoRA to. If not set, a default list of modules will"
              " be used. By default, LoRA will be applied to all conv and linear layers."
          ),
      )
      parser.add_argument(
          "--vae_encode_batch_size",
          type=int,
          default=32,
          required=False,
          help=(
              "The batch size used when encoding (and decoding) images to latents (and vice versa) using the VAE."
              " Encoding or decoding the whole batch at once may run into OOM issues."
          ),
      )
      parser.add_argument(
          "--timestep_scaling_factor",
          type=float,
          default=10.0,
          help=(
              "The multiplicative timestep scaling factor used when calculating the boundary scalings for LCM. The"
              " higher the scaling is, the lower the approximation error, but the default value of 10.0 should typically"
              " suffice."
          ),
      )
      # ----Mixed Precision----
      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(
          "--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(
          "--cast_teacher_unet",
          action="store_true",
          help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
      )
      # ----Training Optimizations----
      parser.add_argument(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
      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.",
      )
      # ----Distributed Training----
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      # ----------Validation Arguments----------
      parser.add_argument(
          "--validation_steps",
          type=int,
          default=200,
          help="Run validation every X steps.",
      )
      # ----------Huggingface Hub Arguments-----------
      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`.",
      )
      # ----------Accelerate Arguments----------
      parser.add_argument(
          "--tracker_project_name",
          type=str,
          default="text2image-fine-tune",
          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"
          ),
      )
  
      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.proportion_empty_prompts < 0
          or args.proportion_empty_prompts > 1
      ):
          raise ValueError(
              "`--proportion_empty_prompts` must be in the range [0, 1]."
          )
  
      return args
  
imports: import argparse
  import functools
  import gc
  import itertools
  import json
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  from typing import List, Union
  import accelerate
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import torchvision.transforms.functional
  import transformers
  import webdataset
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from braceexpand import braceexpand
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
  from torch.utils.data import default_collate
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
  from webdataset.tariterators import base_plus_ext, tar_file_expander, url_opener, valid_sample
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import resolve_interpolation_mode
  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 14 for calling a global function with example usage: parse_args() and returns: args