#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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 a 🤗 Flax Transformers model on token classification tasks (NER, POS, CHUNKS)"""
import json
import logging
import os
import random
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple

import datasets
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
from tqdm import tqdm

import jax
import jax.numpy as jnp
import optax
import transformers
from flax import struct, traverse_util
from flax.jax_utils import replicate, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
from transformers import (
    AutoConfig,
    AutoTokenizer,
    FlaxAutoModelForTokenClassification,
    HfArgumentParser,
    is_tensorboard_available,
)
from transformers.file_utils import get_full_repo_name
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


logger = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.16.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")

Array = Any
Dataset = datasets.arrow_dataset.Dataset
PRNGKey = Any


@dataclass
class TrainingArguments:
    output_dir: str = field(
        metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
    )
    overwrite_output_dir: bool = field(
        default=False,
        metadata={
            "help": (
                "Overwrite the content of the output directory. "
                "Use this to continue training if output_dir points to a checkpoint directory."
            )
        },
    )
    do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
    do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
    per_device_train_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
    )
    per_device_eval_batch_size: int = field(
        default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
    )
    learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
    weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
    adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
    adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
    adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
    adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
    num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
    warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
    logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
    save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
    eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
    seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
    push_to_hub: bool = field(
        default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
    )
    hub_model_id: str = field(
        default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
    )
    hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})

    def __post_init__(self):
        if self.output_dir is not None:
            self.output_dir = os.path.expanduser(self.output_dir)

    def to_dict(self):
        """
        Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
        the token values by removing their value.
        """
        d = asdict(self)
        for k, v in d.items():
            if isinstance(v, Enum):
                d[k] = v.value
            if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
                d[k] = [x.value for x in v]
            if k.endswith("_token"):
                d[k] = f"<{k.upper()}>"
        return d


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    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 huggingface.co"},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
            "with private models)."
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, pos...)."})
    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 csv or JSON file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate on (a csv or JSON file)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input test data file to predict on (a csv or JSON file)."},
    )
    text_column_name: Optional[str] = field(
        default=None, metadata={"help": "The column name of text to input in the file (a csv or JSON file)."}
    )
    label_column_name: Optional[str] = field(
        default=None, metadata={"help": "The column name of label to input in the file (a csv or JSON file)."}
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_seq_length: int = field(
        default=None,
        metadata={
            "help": "The maximum total input sequence length after tokenization. If set, sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
            "value if set."
        },
    )
    label_all_tokens: bool = field(
        default=False,
        metadata={
            "help": "Whether to put the label for one word on all tokens of generated by that word or just on the "
            "one (in which case the other tokens will have a padding index)."
        },
    )
    return_entity_level_metrics: bool = field(
        default=False,
        metadata={"help": "Whether to return all the entity levels during evaluation or just the overall ones."},
    )

    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"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
        self.task_name = self.task_name.lower()


def create_train_state(
    model: FlaxAutoModelForTokenClassification,
    learning_rate_fn: Callable[[int], float],
    num_labels: int,
    training_args: TrainingArguments,
) -> train_state.TrainState:
    """Create initial training state."""

    class TrainState(train_state.TrainState):
        """Train state with an Optax optimizer.

        The two functions below differ depending on whether the task is classification
        or regression.

        Args:
          logits_fn: Applied to last layer to obtain the logits.
          loss_fn: Function to compute the loss.
        """

        logits_fn: Callable = struct.field(pytree_node=False)
        loss_fn: Callable = struct.field(pytree_node=False)

    # 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.
    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)

    tx = optax.adamw(
        learning_rate=learning_rate_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,
    )

    def cross_entropy_loss(logits, labels):
        xentropy = optax.softmax_cross_entropy(logits, onehot(labels, num_classes=num_labels))
        return jnp.mean(xentropy)

    return TrainState.create(
        apply_fn=model.__call__,
        params=model.params,
        tx=tx,
        logits_fn=lambda logits: logits.argmax(-1),
        loss_fn=cross_entropy_loss,
    )


def create_learning_rate_fn(
    train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.array]:
    """Returns a linear warmup, linear_decay learning rate function."""
    steps_per_epoch = train_ds_size // train_batch_size
    num_train_steps = steps_per_epoch * num_train_epochs
    warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
    decay_fn = optax.linear_schedule(
        init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
    )
    schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
    return schedule_fn


def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
    """Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
    steps_per_epoch = len(dataset) // batch_size
    perms = jax.random.permutation(rng, len(dataset))
    perms = perms[: steps_per_epoch * batch_size]  # Skip incomplete batch.
    perms = perms.reshape((steps_per_epoch, batch_size))

    for perm in perms:
        batch = dataset[perm]
        batch = {k: np.array(v) for k, v in batch.items()}
        batch = shard(batch)

        yield batch


def eval_data_collator(dataset: Dataset, batch_size: int):
    """Returns batches of size `batch_size` from `eval dataset`, sharded over all local devices."""
    for i in range(len(dataset) // batch_size):
        batch = dataset[i * batch_size : (i + 1) * batch_size]
        batch = {k: np.array(v) for k, v in batch.items()}
        batch = shard(batch)

        yield batch


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()

    # 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:
        if training_args.hub_model_id is None:
            repo_name = get_full_repo_name(
                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
            )
        else:
            repo_name = training_args.hub_model_id
        repo = Repository(training_args.output_dir, clone_from=repo_name)

    # 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 for token classification task 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 'tokens' or the first column if no column called
    # 'tokens' is found. 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.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
        )
    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, cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    if raw_datasets["train"] is not None:
        column_names = raw_datasets["train"].column_names
        features = raw_datasets["train"].features
    else:
        column_names = raw_datasets["validation"].column_names
        features = raw_datasets["validation"].features

    if data_args.text_column_name is not None:
        text_column_name = data_args.text_column_name
    elif "tokens" in column_names:
        text_column_name = "tokens"
    else:
        text_column_name = column_names[0]

    if data_args.label_column_name is not None:
        label_column_name = data_args.label_column_name
    elif f"{data_args.task_name}_tags" in column_names:
        label_column_name = f"{data_args.task_name}_tags"
    else:
        label_column_name = column_names[1]

    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
    # unique labels.
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
            unique_labels = unique_labels | set(label)
        label_list = list(unique_labels)
        label_list.sort()
        return label_list

    if isinstance(features[label_column_name].feature, ClassLabel):
        label_list = features[label_column_name].feature.names
        # No need to convert the labels since they are already ints.
        label_to_id = {i: i for i in range(len(label_list))}
    else:
        label_list = get_label_list(raw_datasets["train"][label_column_name])
        label_to_id = {l: i for i, l in enumerate(label_list)}
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        label2id=label_to_id,
        id2label={i: l for l, i in label_to_id.items()},
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
    if config.model_type in {"gpt2", "roberta"}:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name_or_path,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
            add_prefix_space=True,
        )
    else:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_name_or_path,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    model = FlaxAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Preprocessing the datasets
    # Tokenize all texts and align the labels with them.
    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[text_column_name],
            max_length=data_args.max_seq_length,
            padding="max_length",
            truncation=True,
            # We use this argument because the texts in our dataset are lists of words (with a label for each word).
            is_split_into_words=True,
        )

        labels = []

        for i, label in enumerate(examples[label_column_name]):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            previous_word_idx = None
            label_ids = []
            for word_idx in word_ids:
                # Special tokens have a word id that is None. We set the label to -100 so they are automatically
                # ignored in the loss function.
                if word_idx is None:
                    label_ids.append(-100)
                # We set the label for the first token of each word.
                elif word_idx != previous_word_idx:
                    label_ids.append(label_to_id[label[word_idx]])
                # For the other tokens in a word, we set the label to either the current label or -100, depending on
                # the label_all_tokens flag.
                else:
                    label_ids.append(label_to_id[label[word_idx]] if data_args.label_all_tokens else -100)
                previous_word_idx = word_idx

            labels.append(label_ids)
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    processed_raw_datasets = raw_datasets.map(
        tokenize_and_align_labels,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
        remove_columns=raw_datasets["train"].column_names,
        desc="Running tokenizer on dataset",
    )

    train_dataset = processed_raw_datasets["train"]
    eval_dataset = processed_raw_datasets["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 = training_args.per_device_train_batch_size * jax.local_device_count()
    eval_batch_size = training_args.per_device_eval_batch_size * jax.local_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, num_labels=num_labels, training_args=training_args)

    # 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")

    metric = load_metric("seqeval")

    def get_labels(y_pred, y_true):
        # Transform predictions and references tensos to numpy arrays

        # Remove ignored index (special tokens)
        true_predictions = [
            [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100]
            for pred, gold_label in zip(y_pred, y_true)
        ]
        true_labels = [
            [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100]
            for pred, gold_label in zip(y_pred, y_true)
        ]
        return true_predictions, true_labels

    def compute_metrics():
        results = metric.compute()
        if data_args.return_entity_level_metrics:
            # Unpack nested dictionaries
            final_results = {}
            for key, value in results.items():
                if isinstance(value, dict):
                    for n, v in value.items():
                        final_results[f"{key}_{n}"] = v
                else:
                    final_results[key] = value
            return final_results
        else:
            return {
                "precision": results["overall_precision"],
                "recall": results["overall_recall"],
                "f1": results["overall_f1"],
                "accuracy": results["overall_accuracy"],
            }

    logger.info(f"===== Starting training ({num_epochs} epochs) =====")
    train_time = 0

    # make sure weights are replicated on each device
    state = replicate(state)

    train_time = 0
    step_per_epoch = len(train_dataset) // train_batch_size
    total_steps = step_per_epoch * num_epochs
    epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{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
        for step, batch in enumerate(
            tqdm(
                train_data_collator(input_rng, train_dataset, train_batch_size),
                total=step_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 * step_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: {train_metric['learning_rate']})"
                )

                train_metrics = []

            if cur_step % training_args.eval_steps == 0 and cur_step > 0:

                eval_metrics = {}
                # evaluate
                for batch in tqdm(
                    eval_data_collator(eval_dataset, eval_batch_size),
                    total=len(eval_dataset) // eval_batch_size,
                    desc="Evaluating ...",
                    position=2,
                ):
                    labels = batch.pop("labels")
                    predictions = p_eval_step(state, batch)
                    predictions = np.array([pred for pred in chain(*predictions)])
                    labels = np.array([label for label in chain(*labels)])
                    labels[np.array(chain(*batch["attention_mask"])) == 0] = -100
                    preds, refs = get_labels(predictions, labels)
                    metric.add_batch(
                        predictions=preds,
                        references=refs,
                    )

                # evaluate also on leftover examples (not divisible by batch_size)
                num_leftover_samples = len(eval_dataset) % eval_batch_size

                # make sure leftover batch is evaluated on one device
                if num_leftover_samples > 0 and jax.process_index() == 0:
                    # take leftover samples
                    batch = eval_dataset[-num_leftover_samples:]
                    batch = {k: np.array(v) for k, v in batch.items()}

                    labels = batch.pop("labels")
                    predictions = eval_step(unreplicate(state), batch)
                    labels = np.array(labels)
                    labels[np.array(batch["attention_mask"]) == 0] = -100
                    preds, refs = get_labels(predictions, labels)
                    metric.add_batch(
                        predictions=preds,
                        references=refs,
                    )

                eval_metrics = compute_metrics()

                if data_args.return_entity_level_metrics:
                    logger.info(f"Step... ({cur_step}/{total_steps} | Validation metrics: {eval_metrics}")
                else:
                    logger.info(
                        f"Step... ({cur_step}/{total_steps} | Validation f1: {eval_metrics['f1']}, Validation Acc: {eval_metrics['accuracy']})"
                    )

                if has_tensorboard and jax.process_index() == 0:
                    write_eval_metric(summary_writer, eval_metrics, 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}"

    # Eval after training
    if training_args.do_eval:
        eval_metrics = {}
        eval_loader = eval_data_collator(eval_dataset, eval_batch_size)
        for batch in tqdm(eval_loader, total=len(eval_dataset) // eval_batch_size, desc="Evaluating ...", position=2):
            labels = batch.pop("labels")
            predictions = p_eval_step(state, batch)
            predictions = np.array([pred for pred in chain(*predictions)])
            labels = np.array([label for label in chain(*labels)])
            labels[np.array(chain(*batch["attention_mask"])) == 0] = -100
            preds, refs = get_labels(predictions, labels)
            metric.add_batch(predictions=preds, references=refs)

        # evaluate also on leftover examples (not divisible by batch_size)
        num_leftover_samples = len(eval_dataset) % eval_batch_size

        # make sure leftover batch is evaluated on one device
        if num_leftover_samples > 0 and jax.process_index() == 0:
            # take leftover samples
            batch = eval_dataset[-num_leftover_samples:]
            batch = {k: np.array(v) for k, v in batch.items()}

            labels = np.array(batch.pop("labels"))
            predictions = eval_step(unreplicate(state), batch)
            labels[np.array(batch["attention_mask"]) == 0] = -100
            preds, refs = get_labels(predictions, labels)
            metric.add_batch(predictions=preds, references=refs)

        eval_metrics = compute_metrics()

        if jax.process_index() == 0:
            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
            path = os.path.join(training_args.output_dir, "eval_results.json")
            with open(path, "w") as f:
                json.dump(eval_metrics, f, indent=4, sort_keys=True)


if __name__ == "__main__":
    main()
