usrbinenv python codingutf8 2021 the huggingface team 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 orglicenseslicense2 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 finetuning 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 finetuned by this script https huggingface comodels filterfillmask you can also adapt this script on your own masked language modeling task pointers for this are left as comments arguments pertaining to which modelconfigtokenizer we are going to finetune or train from scratch arguments pertaining to what data we are going to input our model for training and eval 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 mlmprobability obj float optional defaults to 0 15 the probability with which to randomly mask tokens in the input note for best performance this data collator should be used with a dataset having items that are dictionaries or batchencoding with the obj specialtokensmask key as returned by a class transformers pretrainedtokenizer or a class transformers pretrainedtokenizerfast with the argument obj returnspecialtokensmasktrue handle dict or lists with proper padding and conversion to tensor if special token mask has been preprocessed pop it from the dict prepare masked tokens inputslabels for masked language modeling 80 mask 10 random 10 original we sample a few tokens in each sequence for mlm training with probability self mlmprobability 80 of the time we replace masked input tokens with tokenizer masktoken mask 10 of the time we replace masked input tokens with random word the rest of the time 10 of the time we keep the masked input tokens unchanged the training iterator is advanced so that after groupifying the samples numsamples of length maxseqlength are returned concatenate tokenized samples to list excluding id and text concatenated tokens are split to lists of length maxseqlength note that remainedr of maxseqlength are thrown away see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns 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 setup logging log on each process the small summary set the verbosity to info of the transformers logger on main process only set seed before initializing model get the datasets you can either provide your own csvjsontxt training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface codatasets the dataset will be downloaded automatically from the datasets hub for csvjson 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 downloading and loading a dataset from the hub otherwise we tokenize every text then concatenate them together before splitting them in smaller parts we use returnspecialtokensmasktrue because datacollatorforlanguagemodeling see below is more efficient when it receives the specialtokensmask data collator this one will take care of randomly masking the tokens initialize our training store some constant define number steps per stream epoch create learning rate schedule we use optax s masking functionality to not apply weight decay to bias and layernorm scale parameters decaymaskfn returns a mask boolean with the same structure as the parameters the mask is true for parameters that should be decayed note that this mask is specifically adapted for flaxbertlike models for other models one should correct the layer norm parameter naming accordingly create adam optimizer setup train state define gradient update step fn compute loss ignore padded input tokens take average create parallel version of the train step define eval fn compute loss ignore padded input tokens compute accuracy summarize metrics replicate the train state on each device training once the end of the dataset stream is reached the training iterator is reinitialized and reshuffled and a new eval dataset is randomly chosen process input samples model forward evaluating avoid using jax numpy here in case of tpu training process input samples model forward normalize eval metrics update progress bar save checkpoint after each epoch and push checkpoint to the hub update tqdm bar usr bin env python coding utf 8 2021 the huggingface team 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 fill mask you can also adapt this script on your own masked language modeling task pointers for this are left as comments arguments pertaining to which model config tokenizer we are going to fine tune or train from scratch arguments pertaining to what data we are going to input our model for training and eval 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_probability obj float optional defaults to 0 15 the probability with which to randomly mask tokens in the input 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 handle dict or lists with proper padding and conversion to tensor if special token mask has been preprocessed pop it from the dict prepare masked tokens inputs labels for masked language modeling 80 mask 10 random 10 original we sample a few tokens in each sequence for mlm training with probability self mlm_probability we only compute loss on masked tokens 80 of the time we replace masked input tokens with tokenizer mask_token mask 10 of the time we replace masked input tokens with random word the rest of the time 10 of the time we keep the masked input tokens unchanged the training iterator is advanced so that after groupifying the samples num_samples of length max_seq_length are returned concatenate tokenized samples to list excluding id and text concatenated tokens are split to lists of length max_seq_length note that remainedr of max_seq_length are thrown away 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 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 setup logging log on each process the small summary set the verbosity to info of the transformers logger on main process only set seed before initializing model 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 downloading and loading a dataset from the hub otherwise we tokenize every text then concatenate them together before splitting them in smaller parts we use return_special_tokens_mask true because datacollatorforlanguagemodeling see below is more efficient when it receives the special_tokens_mask data collator this one will take care of randomly masking the tokens initialize our training store some constant define number steps per stream epoch create learning rate schedule we use optax s masking functionality to not apply weight decay to bias and layernorm scale parameters decay_mask_fn returns a mask boolean with the same structure as the parameters the mask is true for parameters that should be decayed note that this mask is specifically adapted for flaxbert like models for other models one should correct the layer norm parameter naming accordingly create adam optimizer setup train state define gradient update step fn compute loss ignore padded input tokens take average create parallel version of the train step define eval fn compute loss ignore padded input tokens compute accuracy summarize metrics replicate the train state on each device training once the end of the dataset stream is reached the training iterator is reinitialized and reshuffled and a new eval dataset is randomly chosen process input samples model forward evaluating avoid using jax numpy here in case of tpu training process input samples model forward normalize eval metrics update progress bar save checkpoint after each epoch and push checkpoint to the hub update tqdm bar
import logging
import os
import sys
import time
from collections import defaultdict
from dataclasses import dataclass, field


from pathlib import Path
from typing import Dict, List, Optional, Tuple

import datasets
import flax
import jax
import jax.numpy as jnp
import numpy as np
import optax
from datasets import load_dataset
from flax import jax_utils, traverse_util
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from tqdm import tqdm

from transformers import (
    CONFIG_MAPPING,
    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoTokenizer,
    FlaxAutoModelForMaskedLM,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    TensorType,
    TrainingArguments,
    is_tensorboard_available,
    set_seed,
)


if datasets.__version__ <= "1.8.0":
    raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming")


MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class ModelArguments:
    

    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."},
    )
    dtype: Optional[str] = field(
        default="float32",
        metadata={
            "help": (
                "Floating-point format in which the model weights should be initialized and trained. Choose one of"
                " `[float32, float16, bfloat16]`."
            )
        },
    )


@dataclass
class DataTrainingArguments:
    

    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."
            )
        },
    )
    line_by_line: bool = field(
        default=False,
        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
    )
    text_column_name: str = field(
        default="text", metadata={"help": "The name of the column to retrieve the training text."}
    )
    shuffle_buffer_size: int = field(
        default=10000, metadata={"help": "The number of examples to pre-load for shuffling."}
    )
    num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."})
    num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"})

    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."


@flax.struct.dataclass
class FlaxDataCollatorForLanguageModeling:
    

    tokenizer: PreTrainedTokenizerBase
    mlm_probability: float = 0.15

    def __post_init__(self):
        if 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]]) -> Dict[str, np.ndarray]:
        
        batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY)

        
        special_tokens_mask = batch.pop("special_tokens_mask", None)

        batch["input_ids"], batch["labels"] = self.mask_tokens(
            batch["input_ids"], special_tokens_mask=special_tokens_mask
        )
        return batch

    def mask_tokens(
        self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray]
    ) -> Tuple[jnp.ndarray, jnp.ndarray]:
        
        labels = inputs.copy()
        
        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  

        
        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)

        
        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]

        
        return inputs, labels


def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray:
    num_samples = len(samples_idx)
    samples_to_remove = num_samples % batch_size

    if samples_to_remove != 0:
        samples_idx = samples_idx[:-samples_to_remove]
    sections_split = num_samples // batch_size
    batch_idx = np.split(samples_idx, sections_split)
    return batch_idx


def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
    
    num_total_tokens = max_seq_length * num_samples
    samples = defaultdict(list)

    i = 0
    while i < num_total_tokens:
        tokenized_samples = next(train_iterator)
        i += len(tokenized_samples["input_ids"])

        
        samples = {
            k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"]
        }

    
    
    def group_texts(examples):
        result = {
            k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)]
            for k, t in examples.items()
        }
        return result

    grouped_samples = group_texts(samples)
    return grouped_samples


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)


if __name__ == "__main__":
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        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."
        )

    
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        level="INFO",
        datefmt="[%X]",
    )

    
    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}"
    )

    
    logger.info(f"Training/evaluation parameters {training_args}")

    
    set_seed(training_args.seed)

    
    
    
    
    
    
    if data_args.dataset_name is not None:
        
        dataset = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            streaming=True,
            split="train",
        )

    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."
        )

    
    
    
    def tokenize_function(examples):
        return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True)

    tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys()))

    shuffle_seed = training_args.seed
    tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed)

    has_tensorboard = is_tensorboard_available()
    if has_tensorboard and jax.process_index() == 0:
        try:
            from flax.metrics.tensorboard import SummaryWriter
        except ImportError as ie:
            has_tensorboard = False
            logger.warning(
                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
            )

        summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))

    
    
    data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)

    
    rng = jax.random.PRNGKey(training_args.seed)
    dropout_rngs = jax.random.split(rng, jax.local_device_count())

    if model_args.model_name_or_path:
        model = FlaxAutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )
    else:
        model = FlaxAutoModelForMaskedLM.from_config(
            config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
        )

    
    num_epochs = int(training_args.num_train_epochs)
    train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
    eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()

    
    num_train_steps = data_args.num_train_steps

    
    warmup_fn = optax.linear_schedule(
        init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
    )
    decay_fn = optax.linear_schedule(
        init_value=training_args.learning_rate,
        end_value=0,
        transition_steps=num_train_steps - training_args.warmup_steps,
    )
    linear_decay_lr_schedule_fn = optax.join_schedules(
        schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
    )

    
    
    
    
    
    
    
    def decay_mask_fn(params):
        flat_params = traverse_util.flatten_dict(params)
        flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params}
        return traverse_util.unflatten_dict(flat_mask)

    
    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,
        mask=decay_mask_fn,
    )

    
    state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw)

    
    def train_step(state, batch, dropout_rng):
        dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)

        def loss_fn(params):
            labels = batch.pop("labels")

            logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]

            
            label_mask = jnp.where(labels > 0, 1.0, 0.0)
            loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask

            
            loss = loss.sum() / label_mask.sum()

            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": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
        )

        return new_state, metrics, new_dropout_rng

    
    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))

    
    def eval_step(params, batch):
        labels = batch.pop("labels")

        logits = model(**batch, params=params, train=False)[0]

        
        label_mask = jnp.where(labels > 0, 1.0, 0.0)
        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask

        
        accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask

        
        metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()}
        metrics = jax.lax.psum(metrics, axis_name="batch")

        return metrics

    p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))

    
    state = jax_utils.replicate(state)

    train_time = 0
    train_start = time.time()
    train_metrics = []
    eval_metrics = []

    training_iter = iter(tokenized_datasets)

    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
    eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)

    steps = tqdm(range(num_train_steps), desc="Training...", position=0)
    for step in range(num_train_steps):
        
        try:
            samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)
        except StopIteration:
            
            
            shuffle_seed += 1
            tokenized_datasets.set_epoch(shuffle_seed)

            training_iter = iter(tokenized_datasets)

            eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length)
            samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length)

        
        model_inputs = data_collator(samples)

        
        model_inputs = shard(model_inputs.data)
        state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)

        train_metrics.append(train_metric)

        if step % training_args.logging_steps == 0 and step > 0:
            steps.write(
                f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
                f" {train_metric['learning_rate'].mean()})"
            )
            train_time += time.time() - train_start
            if has_tensorboard and jax.process_index() == 0:
                write_train_metric(summary_writer, train_metrics, train_time, step)
            train_metrics = []

        
        if step % training_args.eval_steps == 0 and step > 0:
            
            eval_samples_idx = np.arange(data_args.num_eval_samples)
            eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)

            for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)):
                
                batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()}
                model_inputs = data_collator(batch_eval_samples)

                
                model_inputs = shard(model_inputs.data)
                metrics = p_eval_step(state.params, model_inputs)
                eval_metrics.append(metrics)

            
            eval_metrics = get_metrics(eval_metrics)
            eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
            eval_normalizer = eval_metrics.pop("normalizer")
            eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)

            
            steps.desc = (
                f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:"
                f" {eval_metrics['accuracy']})"
            )

            if has_tensorboard and jax.process_index() == 0:
                write_eval_metric(summary_writer, eval_metrics, step)
            eval_metrics = []

            
            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,
                    push_to_hub=training_args.push_to_hub,
                    commit_message=f"Saving weights and logs of step {step+1}",
                )

        
        steps.update(1)