code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of finetuning Kandinsky 2.2."
      )
      parser.add_argument(
          "--pretrained_decoder_model_name_or_path",
          type=str,
          default="kandinsky-community/kandinsky-2-2-decoder",
          required=False,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--pretrained_prior_model_name_or_path",
          type=str,
          default="kandinsky-community/kandinsky-2-2-prior",
          required=False,
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      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 an 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(
          "--validation_prompt",
          type=str,
          default=None,
          help="A prompt that is sampled during training for inference.",
      )
      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=1,
          help=(
              "Run fine-tuning validation every X epochs. The validation process consists of running the prompt"
              " `args.validation_prompt` multiple times: `args.num_validation_images`."
          ),
      )
      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(
          "--output_dir",
          type=str,
          default="kandi_2_2-model-finetuned-lora",
          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=1,
          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(
          "--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="learning rate",
      )
      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(
          "--snr_gamma",
          type=float,
          default=None,
          help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
          "More details here: https://arxiv.org/abs/2303.09556.",
      )
      parser.add_argument(
          "--use_8bit_adam",
          action="store_true",
          help="Whether or not to use 8-bit Adam from bitsandbytes.",
      )
      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(
          "--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=0.0,
          required=False,
          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(
          "--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(
          "--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(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      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(
          "--rank",
          type=int,
          default=4,
          help=("The dimension of the LoRA update matrices."),
      )
  
      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
  
      # Sanity checks
      if args.dataset_name is None and args.train_data_dir is None:
          raise ValueError(
              "Need either a dataset name or a training folder."
          )
  
      return args
  
imports: import argparse
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  import datasets
  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 tqdm import tqdm
  from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  import diffusers
  from diffusers import AutoPipelineForText2Image, DDPMScheduler, PriorTransformer
  from diffusers.loaders import AttnProcsLayers
  from diffusers.models.attention_processor import LoRAAttnProcessor
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import compute_snr
  from diffusers.utils import check_min_version, is_wandb_available
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args