code: def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (ModelArguments, DataTrainingArguments, TrainingArguments)
      )
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          model_args, data_args, training_args = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
          ) = parser.parse_args_into_dataclasses()
  
      if model_args.use_auth_token is not None:
          warnings.warn(
              "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
              FutureWarning,
          )
          if model_args.token is not None:
              raise ValueError(
                  "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
              )
          model_args.token = model_args.use_auth_token
  
      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry(
          "run_image_classification",
          model_args,
          data_args,
          framework="flax",
      )
  
      if (
          os.path.exists(training_args.output_dir)
          and os.listdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          raise ValueError(
              f"Output directory ({training_args.output_dir}) already exists and is not empty. "
              "Use --overwrite_output_dir to overcome."
          )
  
      # Make one log on every process with the configuration for debugging.
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          level=logging.INFO,
      )
      # Setup logging, we only want one process per machine to log things on the screen.
      logger.setLevel(
          logging.INFO if jax.process_index() == 0 else logging.ERROR
      )
      if jax.process_index() == 0:
          transformers.utils.logging.set_verbosity_info()
      else:
          transformers.utils.logging.set_verbosity_error()
  
      # Set the verbosity to info of the Transformers logger (on main process only):
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # set seed for random transforms and torch dataloaders
      set_seed(training_args.seed)
  
      # Handle the repository creation
      if training_args.push_to_hub:
          # Retrieve of infer repo_name
          repo_name = training_args.hub_model_id
          if repo_name is None:
              repo_name = Path(training_args.output_dir).absolute().name
          # Create repo and retrieve repo_id
          repo_id = create_repo(
              repo_name, exist_ok=True, token=training_args.hub_token
          ).repo_id
          # Clone repo locally
          repo = Repository(
              training_args.output_dir,
              clone_from=repo_id,
              token=training_args.hub_token,
          )
  
      # Initialize datasets and pre-processing transforms
      # We use torchvision here for faster pre-processing
      # Note that here we are using some default pre-processing, for maximum accuray
      # one should tune this part and carefully select what transformations to use.
      normalize = transforms.Normalize(
          mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
      )
      train_dataset = torchvision.datasets.ImageFolder(
          data_args.train_dir,
          transforms.Compose(
              [
                  transforms.RandomResizedCrop(data_args.image_size),
                  transforms.RandomHorizontalFlip(),
                  transforms.ToTensor(),
                  normalize,
              ]
          ),
      )
  
      eval_dataset = torchvision.datasets.ImageFolder(
          data_args.validation_dir,
          transforms.Compose(
              [
                  transforms.Resize(data_args.image_size),
                  transforms.CenterCrop(data_args.image_size),
                  transforms.ToTensor(),
                  normalize,
              ]
          ),
      )
  
      # Load pretrained model and tokenizer
      if model_args.config_name:
          config = AutoConfig.from_pretrained(
              model_args.config_name,
              num_labels=len(train_dataset.classes),
              image_size=data_args.image_size,
              cache_dir=model_args.cache_dir,
              token=model_args.token,
              trust_remote_code=model_args.trust_remote_code,
          )
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(
              model_args.model_name_or_path,
              num_labels=len(train_dataset.classes),
              image_size=data_args.image_size,
              cache_dir=model_args.cache_dir,
              token=model_args.token,
              trust_remote_code=model_args.trust_remote_code,
          )
      else:
          config = CONFIG_MAPPING[model_args.model_type]()
          logger.warning(
              "You are instantiating a new config instance from scratch."
          )
  
      if model_args.model_name_or_path:
          model = FlaxAutoModelForImageClassification.from_pretrained(
              model_args.model_name_or_path,
              config=config,
              seed=training_args.seed,
              dtype=getattr(jnp, model_args.dtype),
              token=model_args.token,
              trust_remote_code=model_args.trust_remote_code,
          )
      else:
          model = FlaxAutoModelForImageClassification.from_config(
              config,
              seed=training_args.seed,
              dtype=getattr(jnp, model_args.dtype),
              trust_remote_code=model_args.trust_remote_code,
          )
  
      # Store some constant
      num_epochs = int(training_args.num_train_epochs)
      train_batch_size = (
          int(training_args.per_device_train_batch_size)
          * jax.device_count()
      )
      per_device_eval_batch_size = int(
          training_args.per_device_eval_batch_size
      )
      eval_batch_size = per_device_eval_batch_size * jax.device_count()
      steps_per_epoch = len(train_dataset) // train_batch_size
      total_train_steps = steps_per_epoch * num_epochs
  
      def collate_fn(examples):
          pixel_values = torch.stack(
              [example[0] for example in examples]
          )
          labels = torch.tensor([example[1] for example in examples])
  
          batch = {"pixel_values": pixel_values, "labels": labels}
          batch = {k: v.numpy() for k, v in batch.items()}
  
          return batch
  
      # Create data loaders
      train_loader = torch.utils.data.DataLoader(
          train_dataset,
          batch_size=train_batch_size,
          shuffle=True,
          num_workers=data_args.preprocessing_num_workers,
          persistent_workers=True,
          drop_last=True,
          collate_fn=collate_fn,
      )
  
      eval_loader = torch.utils.data.DataLoader(
          eval_dataset,
          batch_size=eval_batch_size,
          shuffle=False,
          num_workers=data_args.preprocessing_num_workers,
          persistent_workers=True,
          drop_last=False,
          collate_fn=collate_fn,
      )
  
      # Enable tensorboard only on the master node
      has_tensorboard = is_tensorboard_available()
      if has_tensorboard and jax.process_index() == 0:
          try:
              from flax.metrics.tensorboard import SummaryWriter
  
              summary_writer = SummaryWriter(
                  log_dir=Path(training_args.output_dir)
              )
          except ImportError as ie:
              has_tensorboard = False
              logger.warning(
                  f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
              )
      else:
          logger.warning(
              "Unable to display metrics through TensorBoard because the package is not installed: "
              "Please run pip install tensorboard to enable."
          )
  
      # Initialize our training
      rng = jax.random.PRNGKey(training_args.seed)
      rng, dropout_rng = jax.random.split(rng)
  
      # Create learning rate schedule
      linear_decay_lr_schedule_fn = create_learning_rate_fn(
          len(train_dataset),
          train_batch_size,
          training_args.num_train_epochs,
          training_args.warmup_steps,
          training_args.learning_rate,
      )
  
      # create adam optimizer
      adamw = optax.adamw(
          learning_rate=linear_decay_lr_schedule_fn,
          b1=training_args.adam_beta1,
          b2=training_args.adam_beta2,
          eps=training_args.adam_epsilon,
          weight_decay=training_args.weight_decay,
      )
  
      # Setup train state
      state = TrainState.create(
          apply_fn=model.__call__,
          params=model.params,
          tx=adamw,
          dropout_rng=dropout_rng,
      )
  
      def loss_fn(logits, labels):
          loss = optax.softmax_cross_entropy(
              logits, onehot(labels, logits.shape[-1])
          )
          return loss.mean()
  
      # Define gradient update step fn
      def train_step(state, batch):
          dropout_rng, new_dropout_rng = jax.random.split(
              state.dropout_rng
          )
  
          def compute_loss(params):
              labels = batch.pop("labels")
              logits = state.apply_fn(
                  **batch,
                  params=params,
                  dropout_rng=dropout_rng,
                  train=True,
              )[0]
              loss = loss_fn(logits, labels)
              return loss
  
          grad_fn = jax.value_and_grad(compute_loss)
          loss, grad = grad_fn(state.params)
          grad = jax.lax.pmean(grad, "batch")
  
          new_state = state.apply_gradients(
              grads=grad, dropout_rng=new_dropout_rng
          )
  
          metrics = {
              "loss": loss,
              "learning_rate": linear_decay_lr_schedule_fn(state.step),
          }
          metrics = jax.lax.pmean(metrics, axis_name="batch")
  
          return new_state, metrics
  
      # Define eval fn
      def eval_step(params, batch):
          labels = batch.pop("labels")
          logits = model(**batch, params=params, train=False)[0]
          loss = loss_fn(logits, labels)
  
          # summarize metrics
          accuracy = (jnp.argmax(logits, axis=-1) == labels).mean()
          metrics = {"loss": loss, "accuracy": accuracy}
          metrics = jax.lax.pmean(metrics, axis_name="batch")
          return metrics
  
      # Create parallel version of the train and eval step
      p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
      p_eval_step = jax.pmap(eval_step, "batch")
  
      # Replicate the train state on each device
      state = state.replicate()
  
      logger.info("***** Running training *****")
      logger.info(f"  Num examples = {len(train_dataset)}")
      logger.info(f"  Num Epochs = {num_epochs}")
      logger.info(
          f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}"
      )
      logger.info(
          f"  Total train batch size (w. parallel & distributed) = {train_batch_size}"
      )
      logger.info(f"  Total optimization steps = {total_train_steps}")
  
      train_time = 0
      epochs = tqdm(
          range(num_epochs),
          desc=f"Epoch ... (1/{num_epochs})",
          position=0,
      )
      for epoch in epochs:
          # ======================== Training ================================
          train_start = time.time()
  
          # Create sampling rng
          rng, input_rng = jax.random.split(rng)
          train_metrics = []
  
          steps_per_epoch = len(train_dataset) // train_batch_size
          train_step_progress_bar = tqdm(
              total=steps_per_epoch,
              desc="Training...",
              position=1,
              leave=False,
          )
          # train
          for batch in train_loader:
              batch = shard(batch)
              state, train_metric = p_train_step(state, batch)
              train_metrics.append(train_metric)
  
              train_step_progress_bar.update(1)
  
          train_time += time.time() - train_start
  
          train_metric = unreplicate(train_metric)
  
          train_step_progress_bar.close()
          epochs.write(
              f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
              f" {train_metric['learning_rate']})"
          )
  
          # ======================== Evaluating ==============================
          eval_metrics = []
          eval_steps = len(eval_dataset) // eval_batch_size
          eval_step_progress_bar = tqdm(
              total=eval_steps,
              desc="Evaluating...",
              position=2,
              leave=False,
          )
          for batch in eval_loader:
              # Model forward
              metrics = pad_shard_unpad(
                  p_eval_step, static_return=True
              )(
                  state.params,
                  batch,
                  min_device_batch=per_device_eval_batch_size,
              )
              eval_metrics.append(metrics)
  
              eval_step_progress_bar.update(1)
  
          # normalize eval metrics
          eval_metrics = get_metrics(eval_metrics)
          eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
  
          # Print metrics and update progress bar
          eval_step_progress_bar.close()
          desc = (
              f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {round(eval_metrics['loss'].item(), 4)} | "
              f"Eval Accuracy: {round(eval_metrics['accuracy'].item(), 4)})"
          )
          epochs.write(desc)
          epochs.desc = desc
  
          # Save metrics
          if has_tensorboard and jax.process_index() == 0:
              cur_step = epoch * (
                  len(train_dataset) // train_batch_size
              )
              write_metric(
                  summary_writer,
                  train_metrics,
                  eval_metrics,
                  train_time,
                  cur_step,
              )
  
          # save checkpoint after each epoch and push checkpoint to the hub
          if jax.process_index() == 0:
              params = jax.device_get(
                  jax.tree_util.tree_map(lambda x: x[0], state.params)
              )
              model.save_pretrained(
                  training_args.output_dir, params=params
              )
              if training_args.push_to_hub:
                  repo.push_to_hub(
                      commit_message=f"Saving weights and logs of epoch {epoch}",
                      blocking=False,
                  )
  
imports: import logging
  import os
  import sys
  import time
  import warnings
  from dataclasses import asdict, dataclass, field
  from enum import Enum
  from pathlib import Path
  from typing import Callable, Optional
  import jax
  import jax.numpy
  import optax
  import torch
  import torchvision
  import torchvision.transforms
  from flax import jax_utils
  from flax.jax_utils import pad_shard_unpad, unreplicate
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
  from huggingface_hub import Repository, create_repo
  from tqdm import tqdm
  import transformers
  from transformers import CONFIG_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, FlaxAutoModelForImageClassification, HfArgumentParser, is_tensorboard_available, set_seed
  from transformers.utils import send_example_telemetry
label_desc: Use image node_id 3 for calling a global function with example usage: main() and returns: batch, loss, new_state,  metrics, metrics, loss