code: def main():
      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_glue", model_args, data_args, framework="flax"
      )
  
      # 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:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_info()
      else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
  
      # 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,
          )
  
      # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
      # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
  
      # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
      # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
      # label if at least two columns are provided.
  
      # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
      # single column. You can easily tweak this behavior (see below)
  
      # In distributed training, the load_dataset function guarantee that only one local process can concurrently
      # download the dataset.
      if data_args.task_name is not None:
          # Downloading and loading a dataset from the hub.
          raw_datasets = load_dataset(
              "glue",
              data_args.task_name,
              token=model_args.token,
          )
      else:
          # Loading the dataset from local csv or json file.
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
          extension = (
              data_args.train_file
              if data_args.train_file is not None
              else data_args.valid_file
          ).split(".")[-1]
          raw_datasets = load_dataset(
              extension,
              data_files=data_files,
              token=model_args.token,
          )
      # See more about loading any type of standard or custom dataset at
      # https://huggingface.co/docs/datasets/loading_datasets.
  
      # Labels
      if data_args.task_name is not None:
          is_regression = data_args.task_name == "stsb"
          if not is_regression:
              label_list = raw_datasets["train"].features["label"].names
              num_labels = len(label_list)
          else:
              num_labels = 1
      else:
          # Trying to have good defaults here, don't hesitate to tweak to your needs.
          is_regression = raw_datasets["train"].features[
              "label"
          ].dtype in ["float32", "float64"]
          if is_regression:
              num_labels = 1
          else:
              # A useful fast method:
              # https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique
              label_list = raw_datasets["train"].unique("label")
              label_list.sort()  # Let's sort it for determinism
              num_labels = len(label_list)
  
      # Load pretrained model and tokenizer
      config = AutoConfig.from_pretrained(
          model_args.model_name_or_path,
          num_labels=num_labels,
          finetuning_task=data_args.task_name,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          model_args.model_name_or_path,
          use_fast=not model_args.use_slow_tokenizer,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      model = FlaxAutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path,
          config=config,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
  
      # Preprocessing the datasets
      if data_args.task_name is not None:
          sentence1_key, sentence2_key = task_to_keys[
              data_args.task_name
          ]
      else:
          # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
          non_label_column_names = [
              name
              for name in raw_datasets["train"].column_names
              if name != "label"
          ]
          if (
              "sentence1" in non_label_column_names
              and "sentence2" in non_label_column_names
          ):
              sentence1_key, sentence2_key = "sentence1", "sentence2"
          else:
              if len(non_label_column_names) >= 2:
                  sentence1_key, sentence2_key = non_label_column_names[
                      :2
                  ]
              else:
                  sentence1_key, sentence2_key = (
                      non_label_column_names[0],
                      None,
                  )
  
      # Some models have set the order of the labels to use, so let's make sure we do use it.
      label_to_id = None
      if (
          model.config.label2id
          != PretrainedConfig(num_labels=num_labels).label2id
          and data_args.task_name is not None
          and not is_regression
      ):
          # Some have all caps in their config, some don't.
          label_name_to_id = {
              k.lower(): v for k, v in model.config.label2id.items()
          }
          if sorted(label_name_to_id.keys()) == sorted(label_list):
              logger.info(
                  f"The configuration of the model provided the following label correspondence: {label_name_to_id}. "
                  "Using it!"
              )
              label_to_id = {
                  i: label_name_to_id[label_list[i]]
                  for i in range(num_labels)
              }
          else:
              logger.warning(
                  "Your model seems to have been trained with labels, but they don't match the dataset: ",
                  f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
                  "\nIgnoring the model labels as a result.",
              )
      elif data_args.task_name is None:
          label_to_id = {v: i for i, v in enumerate(label_list)}
  
      def preprocess_function(examples):
          # Tokenize the texts
          texts = (
              (examples[sentence1_key],)
              if sentence2_key is None
              else (examples[sentence1_key], examples[sentence2_key])
          )
          result = tokenizer(
              *texts,
              padding="max_length",
              max_length=data_args.max_seq_length,
              truncation=True,
          )
  
          if "label" in examples:
              if label_to_id is not None:
                  # Map labels to IDs (not necessary for GLUE tasks)
                  result["labels"] = [
                      label_to_id[l] for l in examples["label"]
                  ]
              else:
                  # In all cases, rename the column to labels because the model will expect that.
                  result["labels"] = examples["label"]
          return result
  
      processed_datasets = raw_datasets.map(
          preprocess_function,
          batched=True,
          remove_columns=raw_datasets["train"].column_names,
      )
  
      train_dataset = processed_datasets["train"]
      eval_dataset = processed_datasets[
          "validation_matched"
          if data_args.task_name == "mnli"
          else "validation"
      ]
  
      # Log a few random samples from the training set:
      for index in random.sample(range(len(train_dataset)), 3):
          logger.info(
              f"Sample {index} of the training set: {train_dataset[index]}."
          )
  
      # Define a summary writer
      has_tensorboard = is_tensorboard_available()
      if has_tensorboard and jax.process_index() == 0:
          try:
              from flax.metrics.tensorboard import SummaryWriter
  
              summary_writer = SummaryWriter(training_args.output_dir)
              summary_writer.hparams(
                  {
                      **training_args.to_dict(),
                      **vars(model_args),
                      **vars(data_args),
                  }
              )
          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."
          )
  
      def write_train_metric(
          summary_writer, train_metrics, train_time, step
      ):
          summary_writer.scalar("train_time", train_time, step)
  
          train_metrics = get_metrics(train_metrics)
          for key, vals in train_metrics.items():
              tag = f"train_{key}"
              for i, val in enumerate(vals):
                  summary_writer.scalar(
                      tag, val, step - len(vals) + i + 1
                  )
  
      def write_eval_metric(summary_writer, eval_metrics, step):
          for metric_name, value in eval_metrics.items():
              summary_writer.scalar(f"eval_{metric_name}", value, step)
  
      num_epochs = int(training_args.num_train_epochs)
      rng = jax.random.PRNGKey(training_args.seed)
      dropout_rngs = jax.random.split(rng, jax.local_device_count())
  
      train_batch_size = (
          int(training_args.per_device_train_batch_size)
          * jax.local_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()
  
      learning_rate_fn = create_learning_rate_fn(
          len(train_dataset),
          train_batch_size,
          training_args.num_train_epochs,
          training_args.warmup_steps,
          training_args.learning_rate,
      )
  
      state = create_train_state(
          model,
          learning_rate_fn,
          is_regression,
          num_labels=num_labels,
          weight_decay=training_args.weight_decay,
      )
  
      # define step functions
      def train_step(
          state: train_state.TrainState,
          batch: Dict[str, Array],
          dropout_rng: PRNGKey,
      ) -> Tuple[train_state.TrainState, float]:
          """Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
          dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
          targets = batch.pop("labels")
  
          def loss_fn(params):
              logits = state.apply_fn(
                  **batch,
                  params=params,
                  dropout_rng=dropout_rng,
                  train=True,
              )[0]
              loss = state.loss_fn(logits, targets)
              return loss
  
          grad_fn = jax.value_and_grad(loss_fn)
          loss, grad = grad_fn(state.params)
          grad = jax.lax.pmean(grad, "batch")
          new_state = state.apply_gradients(grads=grad)
          metrics = jax.lax.pmean(
              {
                  "loss": loss,
                  "learning_rate": learning_rate_fn(state.step),
              },
              axis_name="batch",
          )
          return new_state, metrics, new_dropout_rng
  
      p_train_step = jax.pmap(
          train_step, axis_name="batch", donate_argnums=(0,)
      )
  
      def eval_step(state, batch):
          logits = state.apply_fn(
              **batch, params=state.params, train=False
          )[0]
          return state.logits_fn(logits)
  
      p_eval_step = jax.pmap(eval_step, axis_name="batch")
  
      if data_args.task_name is not None:
          metric = evaluate.load(
              "glue",
              data_args.task_name,
              cache_dir=model_args.cache_dir,
          )
      else:
          metric = evaluate.load(
              "accuracy", cache_dir=model_args.cache_dir
          )
  
      logger.info(
          f"===== Starting training ({num_epochs} epochs) ====="
      )
      train_time = 0
  
      # make sure weights are replicated on each device
      state = replicate(state)
  
      steps_per_epoch = len(train_dataset) // train_batch_size
      total_steps = steps_per_epoch * num_epochs
      epochs = tqdm(
          range(num_epochs),
          desc=f"Epoch ... (0/{num_epochs})",
          position=0,
      )
      for epoch in epochs:
          train_start = time.time()
          train_metrics = []
  
          # Create sampling rng
          rng, input_rng = jax.random.split(rng)
  
          # train
          train_loader = glue_train_data_collator(
              input_rng, train_dataset, train_batch_size
          )
          for step, batch in enumerate(
              tqdm(
                  train_loader,
                  total=steps_per_epoch,
                  desc="Training...",
                  position=1,
              ),
          ):
              state, train_metric, dropout_rngs = p_train_step(
                  state, batch, dropout_rngs
              )
              train_metrics.append(train_metric)
  
              cur_step = (epoch * steps_per_epoch) + (step + 1)
  
              if (
                  cur_step % training_args.logging_steps == 0
                  and cur_step > 0
              ):
                  # Save metrics
                  train_metric = unreplicate(train_metric)
                  train_time += time.time() - train_start
                  if has_tensorboard and jax.process_index() == 0:
                      write_train_metric(
                          summary_writer,
                          train_metrics,
                          train_time,
                          cur_step,
                      )
  
                  epochs.write(
                      f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate:"
                      f" {train_metric['learning_rate']})"
                  )
  
                  train_metrics = []
  
              if (
                  cur_step % training_args.eval_steps == 0
                  or cur_step % steps_per_epoch == 0
              ) and cur_step > 0:
                  # evaluate
                  eval_loader = glue_eval_data_collator(
                      eval_dataset, eval_batch_size
                  )
                  for batch in tqdm(
                      eval_loader,
                      total=math.ceil(
                          len(eval_dataset) / eval_batch_size
                      ),
                      desc="Evaluating ...",
                      position=2,
                  ):
                      labels = batch.pop("labels")
                      predictions = pad_shard_unpad(p_eval_step)(
                          state,
                          batch,
                          min_device_batch=per_device_eval_batch_size,
                      )
                      metric.add_batch(
                          predictions=np.array(predictions),
                          references=labels,
                      )
  
                  eval_metric = metric.compute()
  
                  logger.info(
                      f"Step... ({cur_step}/{total_steps} | Eval metrics: {eval_metric})"
                  )
  
                  if has_tensorboard and jax.process_index() == 0:
                      write_eval_metric(
                          summary_writer, eval_metric, cur_step
                      )
  
              if (
                  cur_step % training_args.save_steps == 0
                  and cur_step > 0
              ) or (cur_step == total_steps):
                  # save checkpoint after each epoch and push checkpoint to the hub
                  if jax.process_index() == 0:
                      params = jax.device_get(unreplicate(state.params))
                      model.save_pretrained(
                          training_args.output_dir, params=params
                      )
                      tokenizer.save_pretrained(
                          training_args.output_dir
                      )
                      if training_args.push_to_hub:
                          repo.push_to_hub(
                              commit_message=f"Saving weights and logs of step {cur_step}",
                              blocking=False,
                          )
              epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
  
      # save the eval metrics in json
      if jax.process_index() == 0:
          eval_metric = {
              f"eval_{metric_name}": value
              for metric_name, value in eval_metric.items()
          }
          path = os.path.join(
              training_args.output_dir, "eval_results.json"
          )
          with open(path, "w") as f:
              json.dump(eval_metric, f, indent=4, sort_keys=True)
  
imports: import json
  import logging
  import math
  import os
  import random
  import sys
  import time
  import warnings
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Any, Callable, Dict, Optional, Tuple
  import datasets
  import evaluate
  import jax
  import jax.numpy
  import numpy
  import optax
  from datasets import load_dataset
  from flax import struct, traverse_util
  from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard
  from huggingface_hub import Repository, create_repo
  from tqdm import tqdm
  import transformers
  from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, HfArgumentParser, PretrainedConfig, TrainingArguments, is_tensorboard_available
  from transformers.utils import check_min_version, send_example_telemetry
label_desc: Use image node_id 5 for calling a global function with example usage: main() and returns: result, new_state,  metrics,  new_dropout_rng, state, loss