code: # coding=utf-8
  # Copyright 2020 The HuggingFace Team All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a
  text file or a dataset.
  
  Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
  https://huggingface.co/models?filter=masked-lm
  """
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  
  # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  
  import numpy as np
  from datasets import load_dataset
  from tqdm import tqdm
  
  import jax
  import jax.numpy as jnp
  from flax import jax_utils
  from flax.optim import Adam
  from flax.training import common_utils
  from flax.training.common_utils import get_metrics
  from jax.nn import log_softmax
  from transformers import (
      CONFIG_MAPPING,
      MODEL_FOR_MASKED_LM_MAPPING,
      AutoConfig,
      AutoTokenizer,
      FlaxBertForMaskedLM,
      HfArgumentParser,
      PreTrainedTokenizerBase,
      TensorType,
      TrainingArguments,
      is_tensorboard_available,
      set_seed,
  )
  
  
  # Cache the result
  has_tensorboard = is_tensorboard_available()
  if has_tensorboard:
      try:
          from flax.metrics.tensorboard import SummaryWriter
      except ImportError as ie:
          has_tensorboard = False
          print(f"Unable to display metrics through TensorBoard because some package are not installed: {ie}")
  
  else:
      print(
          "Unable to display metrics through TensorBoard because the package is not installed: "
          "Please run pip install tensorboard to enable."
      )
  
  
  MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
  MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
  
  
  @dataclass
  class ModelArguments:
      """
      Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
      """
  
      model_name_or_path: Optional[str] = field(
          default=None,
          metadata={
              "help": "The model checkpoint for weights initialization."
              "Don't set if you want to train a model from scratch."
          },
      )
      model_type: Optional[str] = field(
          default=None,
          metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
      )
      config_name: Optional[str] = field(
          default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
      )
      tokenizer_name: Optional[str] = field(
          default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
      )
      cache_dir: Optional[str] = field(
          default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
      )
      use_fast_tokenizer: bool = field(
          default=True,
          metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
      )
  
  
  @dataclass
  class DataTrainingArguments:
      """
      Arguments pertaining to what data we are going to input our model for training and eval.
      """
  
      dataset_name: Optional[str] = field(
          default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
      )
      dataset_config_name: Optional[str] = field(
          default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
      )
      train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
      validation_file: Optional[str] = field(
          default=None,
          metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
      )
      train_ref_file: Optional[str] = field(
          default=None,
          metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
      )
      validation_ref_file: Optional[str] = field(
          default=None,
          metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
      )
      overwrite_cache: bool = field(
          default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
      )
      validation_split_percentage: Optional[int] = field(
          default=5,
          metadata={
              "help": "The percentage of the train set used as validation set in case there's no validation split"
          },
      )
      max_seq_length: Optional[int] = field(
          default=None,
          metadata={
              "help": "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated. Default to the max input length of the model."
          },
      )
      preprocessing_num_workers: Optional[int] = field(
          default=None,
          metadata={"help": "The number of processes to use for the preprocessing."},
      )
      mlm_probability: float = field(
          default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
      )
      pad_to_max_length: bool = field(
          default=False,
          metadata={
              "help": "Whether to pad all samples to `max_seq_length`. "
              "If False, will pad the samples dynamically when batching to the maximum length in the batch."
          },
      )
  
      def __post_init__(self):
          if self.dataset_name is None and self.train_file is None and self.validation_file is None:
              raise ValueError("Need either a dataset name or a training/validation file.")
          else:
              if self.train_file is not None:
                  extension = self.train_file.split(".")[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
              if self.validation_file is not None:
                  extension = self.validation_file.split(".")[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
  
  
  # Adapted from transformers/data/data_collator.py
  # Letting here for now, let's discuss where it should live
  @dataclass
  class FlaxDataCollatorForLanguageModeling:
      """
      Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
      are not all of the same length.
  
      Args:
          tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
              The tokenizer used for encoding the data.
          mlm (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether or not to use masked language modeling. If set to :obj:`False`, the labels are the same as the
              inputs with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for
              non-masked tokens and the value to predict for the masked token.
          mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
              The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
  
      .. note::
  
          For best performance, this data collator should be used with a dataset having items that are dictionaries or
          BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a
          :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the
          argument :obj:`return_special_tokens_mask=True`.
      """
  
      tokenizer: PreTrainedTokenizerBase
      mlm: bool = True
      mlm_probability: float = 0.15
  
      def __post_init__(self):
          if self.mlm and self.tokenizer.mask_token is None:
              raise ValueError(
                  "This tokenizer does not have a mask token which is necessary for masked language modeling. "
                  "You should pass `mlm=False` to train on causal language modeling instead."
              )
  
      def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]:
          # Handle dict or lists with proper padding and conversion to tensor.
          batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY)
  
          # If special token mask has been preprocessed, pop it from the dict.
          special_tokens_mask = batch.pop("special_tokens_mask", None)
          if self.mlm:
              batch["input_ids"], batch["labels"] = self.mask_tokens(
                  batch["input_ids"], special_tokens_mask=special_tokens_mask
              )
          else:
              labels = batch["input_ids"].copy()
              if self.tokenizer.pad_token_id is not None:
                  labels[labels == self.tokenizer.pad_token_id] = -100
              batch["labels"] = labels
          return batch
  
      def mask_tokens(
          self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
      ) -> Tuple[jnp.ndarray, jnp.ndarray]:
          """
          Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
          """
          labels = inputs.copy()
          # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
          probability_matrix = np.full(labels.shape, self.mlm_probability)
          special_tokens_mask = special_tokens_mask.astype("bool")
  
          probability_matrix[special_tokens_mask] = 0.0
          masked_indices = np.random.binomial(1, probability_matrix).astype("bool")
          labels[~masked_indices] = -100  # We only compute loss on masked tokens
  
          # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
          indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices
          inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)
  
          # 10% of the time, we replace masked input tokens with random word
          indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool")
          indices_random &= masked_indices & ~indices_replaced
  
          random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4")
          inputs[indices_random] = random_words[indices_random]
  
          # The rest of the time (10% of the time) we keep the masked input tokens unchanged
          return inputs, labels
  
  
  def create_learning_rate_scheduler(
      factors="constant * linear_warmup * rsqrt_decay",
      base_learning_rate=0.5,
      warmup_steps=1000,
      decay_factor=0.5,
      steps_per_decay=20000,
      steps_per_cycle=100000,
  ):
      """Creates learning rate schedule.
      Interprets factors in the factors string which can consist of:
      * constant: interpreted as the constant value,
      * linear_warmup: interpreted as linear warmup until warmup_steps,
      * rsqrt_decay: divide by square root of max(step, warmup_steps)
      * rsqrt_normalized_decay: divide by square root of max(step/warmup_steps, 1)
      * decay_every: Every k steps decay the learning rate by decay_factor.
      * cosine_decay: Cyclic cosine decay, uses steps_per_cycle parameter.
      Args:
        factors: string, factors separated by "*" that defines the schedule.
        base_learning_rate: float, the starting constant for the lr schedule.
        warmup_steps: int, how many steps to warm up for in the warmup schedule.
        decay_factor: float, the amount to decay the learning rate by.
        steps_per_decay: int, how often to decay the learning rate.
        steps_per_cycle: int, steps per cycle when using cosine decay.
      Returns:
        a function learning_rate(step): float -> {"learning_rate": float}, the
        step-dependent lr.
      """
      factors = [n.strip() for n in factors.split("*")]
  
      def step_fn(step):
          """Step to learning rate function."""
          ret = 1.0
          for name in factors:
              if name == "constant":
                  ret *= base_learning_rate
              elif name == "linear_warmup":
                  ret *= jnp.minimum(1.0, step / warmup_steps)
              elif name == "rsqrt_decay":
                  ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
              elif name == "rsqrt_normalized_decay":
                  ret *= jnp.sqrt(warmup_steps)
                  ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))
              elif name == "decay_every":
                  ret *= decay_factor ** (step // steps_per_decay)
              elif name == "cosine_decay":
                  progress = jnp.maximum(0.0, (step - warmup_steps) / float(steps_per_cycle))
                  ret *= jnp.maximum(0.0, 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))
              else:
                  raise ValueError("Unknown factor %s." % name)
          return jnp.asarray(ret, dtype=jnp.float32)
  
      return step_fn
  
  
  def compute_metrics(logits, labels, weights, label_smoothing=0.0):
      """Compute summary metrics."""
      loss, normalizer = cross_entropy(logits, labels, weights, label_smoothing)
      acc, _ = accuracy(logits, labels, weights)
      metrics = {"loss": loss, "accuracy": acc, "normalizer": normalizer}
      metrics = jax.lax.psum(metrics, axis_name="batch")
      return metrics
  
  
  def accuracy(logits, targets, weights=None):
      """Compute weighted accuracy for log probs and targets.
      Args:
       logits: [batch, length, num_classes] float array.
       targets: categorical targets [batch, length] int array.
       weights: None or array of shape [batch, length]
      Returns:
        Tuple of scalar loss and batch normalizing factor.
      """
      if logits.ndim != targets.ndim + 1:
          raise ValueError(
              "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
          )
  
      loss = jnp.equal(jnp.argmax(logits, axis=-1), targets)
      loss *= weights
  
      return loss.sum(), weights.sum()
  
  
  def cross_entropy(logits, targets, weights=None, label_smoothing=0.0):
      """Compute cross entropy and entropy for log probs and targets.
      Args:
       logits: [batch, length, num_classes] float array.
       targets: categorical targets [batch, length] int array.
       weights: None or array of shape [batch, length]
       label_smoothing: label smoothing constant, used to determine the on and off values.
      Returns:
        Tuple of scalar loss and batch normalizing factor.
      """
      if logits.ndim != targets.ndim + 1:
          raise ValueError(
              "Incorrect shapes. Got shape %s logits and %s targets" % (str(logits.shape), str(targets.shape))
          )
  
      vocab_size = logits.shape[-1]
      confidence = 1.0 - label_smoothing
      low_confidence = (1.0 - confidence) / (vocab_size - 1)
      normalizing_constant = -(
          confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
      )
      soft_targets = common_utils.onehot(targets, vocab_size, on_value=confidence, off_value=low_confidence)
  
      loss = -jnp.sum(soft_targets * log_softmax(logits), axis=-1)
      loss = loss - normalizing_constant
  
      if weights is not None:
          loss = loss * weights
          normalizing_factor = weights.sum()
      else:
          normalizing_factor = np.prod(targets.shape)
  
      return loss.sum(), normalizing_factor
  
  
  def training_step(optimizer, batch, dropout_rng):
      dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
  
      def loss_fn(params):
          targets = batch.pop("labels")
  
          # Hide away tokens which doesn't participate in the optimization
          token_mask = jnp.where(targets > 0, 1.0, 0.0)
  
          logits = model(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
          loss, weight_sum = cross_entropy(logits, targets, token_mask)
          return loss / weight_sum
  
      step = optimizer.state.step
      lr = lr_scheduler_fn(step)
      grad_fn = jax.value_and_grad(loss_fn)
      loss, grad = grad_fn(optimizer.target)
      grad = jax.lax.pmean(grad, "batch")
      optimizer = optimizer.apply_gradient(grad, learning_rate=lr)
  
      return loss, optimizer, new_dropout_rng
  
  
  def eval_step(params, batch):
      """
      Calculate evaluation metrics on a batch.
      """
      targets = batch.pop("labels")
  
      # Hide away tokens which doesn't participate in the optimization
      token_mask = jnp.where(targets > 0, 1.0, 0.0)
      logits = model(**batch, params=params, train=False)[0]
  
      return compute_metrics(logits, targets, token_mask)
  
  
  def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndarray:
      nb_samples = len(samples_idx)
      samples_to_remove = nb_samples % batch_size
  
      if samples_to_remove != 0:
          samples_idx = samples_idx[:-samples_to_remove]
      sections_split = nb_samples // batch_size
      batch_idx = np.split(samples_idx, sections_split)
      return batch_idx
  
  
  if __name__ == "__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 (
          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."
          )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
          level="NOTSET",
          datefmt="[%X]",
      )
  
      # Log on each process the small summary:
      logger = logging.getLogger(__name__)
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
  
      # Set the verbosity to info of the Transformers logger (on main process only):
      logger.info("Training/evaluation parameters %s", training_args)
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
      # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
      # (the dataset will be downloaded automatically from the datasets Hub).
      #
      # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
      # 'text' is found. You can easily tweak this behavior (see below).
      #
      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
          if "validation" not in datasets.keys():
              datasets["validation"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[:{data_args.validation_split_percentage}%]",
              )
              datasets["train"] = load_dataset(
                  data_args.dataset_name,
                  data_args.dataset_config_name,
                  split=f"train[{data_args.validation_split_percentage}%:]",
              )
      else:
          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.split(".")[-1]
          if extension == "txt":
              extension = "text"
          datasets = load_dataset(extension, data_files=data_files)
      # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # Load pretrained model and tokenizer
  
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      if model_args.config_name:
          config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
      elif model_args.model_name_or_path:
          config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
      else:
          config = CONFIG_MAPPING[model_args.model_type]()
          logger.warning("You are instantiating a new config instance from scratch.")
  
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
          )
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
          )
      else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."
          )
  
      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
          column_names = datasets["train"].column_names
      else:
          column_names = datasets["validation"].column_names
      text_column_name = "text" if "text" in column_names else column_names[0]
  
      padding = "max_length" if data_args.pad_to_max_length else False
  
      def tokenize_function(examples):
          # Remove empty lines
          examples = [line for line in examples if len(line) > 0 and not line.isspace()]
          return tokenizer(
              examples,
              return_special_tokens_mask=True,
              padding=padding,
              truncation=True,
              max_length=data_args.max_seq_length,
          )
  
      tokenized_datasets = datasets.map(
          tokenize_function,
          input_columns=[text_column_name],
          batched=True,
          num_proc=data_args.preprocessing_num_workers,
          remove_columns=column_names,
          load_from_cache_file=not data_args.overwrite_cache,
      )
  
      # Enable tensorboard only on the master node
      if has_tensorboard and jax.host_id() == 0:
          summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir).joinpath("logs").as_posix())
  
      # Data collator
      # This one will take care of randomly masking the tokens.
      data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
  
      # Initialize our training
      rng = jax.random.PRNGKey(training_args.seed)
      dropout_rngs = jax.random.split(rng, jax.local_device_count())
  
      model = FlaxBertForMaskedLM.from_pretrained(
          "bert-base-cased",
          dtype=jnp.float32,
          input_shape=(training_args.train_batch_size, config.max_position_embeddings),
          seed=training_args.seed,
          dropout_rate=0.1,
      )
  
      # Setup optimizer
      optimizer = Adam(
          learning_rate=training_args.learning_rate,
          weight_decay=training_args.weight_decay,
          beta1=training_args.adam_beta1,
          beta2=training_args.adam_beta2,
      ).create(model.params)
  
      # Create learning rate scheduler
      # warmup_steps = 0 causes the Flax optimizer to return NaNs; warmup_steps = 1 is functionally equivalent.
      lr_scheduler_fn = create_learning_rate_scheduler(
          base_learning_rate=training_args.learning_rate, warmup_steps=min(training_args.warmup_steps, 1)
      )
  
      # Create parallel version of the training and evaluation steps
      p_training_step = jax.pmap(training_step, "batch", donate_argnums=(0,))
      p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
  
      # Replicate the optimizer on each device
      optimizer = jax_utils.replicate(optimizer)
  
      # Store some constant
      nb_epochs = int(training_args.num_train_epochs)
      batch_size = int(training_args.train_batch_size)
      eval_batch_size = int(training_args.eval_batch_size)
  
      epochs = tqdm(range(nb_epochs), desc=f"Epoch ... (1/{nb_epochs})", position=0)
      for epoch in epochs:
  
          # ======================== Training ================================
          # Create sampling rng
          rng, training_rng, eval_rng = jax.random.split(rng, 3)
  
          # Generate an epoch by shuffling sampling indices from the train dataset
          nb_training_samples = len(tokenized_datasets["train"])
          training_samples_idx = jax.random.permutation(training_rng, jnp.arange(nb_training_samples))
          training_batch_idx = generate_batch_splits(training_samples_idx, batch_size)
  
          # Gather the indexes for creating the batch and do a training step
          for batch_idx in tqdm(training_batch_idx, desc="Training...", position=1):
              samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
              model_inputs = data_collator(samples, pad_to_multiple_of=16)
  
              # Model forward
              model_inputs = common_utils.shard(model_inputs.data)
              loss, optimizer, dropout_rngs = p_training_step(optimizer, model_inputs, dropout_rngs)
  
          epochs.write(f"Loss: {loss}")
  
          # ======================== Evaluating ==============================
          nb_eval_samples = len(tokenized_datasets["validation"])
          eval_samples_idx = jnp.arange(nb_eval_samples)
          eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
  
          eval_metrics = []
          for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
              samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
              model_inputs = data_collator(samples, pad_to_multiple_of=16)
  
              # Model forward
              model_inputs = common_utils.shard(model_inputs.data)
              metrics = p_eval_step(optimizer.target, model_inputs)
              eval_metrics.append(metrics)
  
          eval_metrics_np = get_metrics(eval_metrics)
          eval_metrics_np = jax.tree_map(jnp.sum, eval_metrics_np)
          eval_normalizer = eval_metrics_np.pop("normalizer")
          eval_summary = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics_np)
  
          # Update progress bar
          epochs.desc = (
              f"Epoch... ({epoch + 1}/{nb_epochs} | Loss: {eval_summary['loss']}, Acc: {eval_summary['accuracy']})"
          )
  
          # Save metrics
          if has_tensorboard and jax.host_id() == 0:
              for name, value in eval_summary.items():
                  summary_writer.scalar(name, value, epoch)
  