"""
Pretraining the library models for T5-like span-masked language modeling on a text file or a dataset.

Here is the full list of checkpoints on the hub that can be pretrained by this script:
https://huggingface.co/models?filter=t5
"""
import json
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from datasets import load_dataset, load_metric
# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional

import numpy as np
from datasets import load_dataset
from tqdm import tqdm

import flax
import jax
import jax.numpy as jnp
import optax
from flax import jax_utils, traverse_util
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard
from huggingface_hub import Repository
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from torch.nn import DataParallel
import torch.distributed as dist
import torch.multiprocessing as mp
import numpy as np
import transformers
from transformers import get_linear_schedule_with_warmup, Conv1D, AdamW
from torch.utils.tensorboard import SummaryWriter
from transformers import (
    CONFIG_MAPPING,
    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
    AutoTokenizer,
    BatchEncoding,
    FlaxT5ForConditionalGeneration,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    T5Config,
    is_tensorboard_available,
    set_seed,
    T5ForConditionalGeneration,
    Trainer
)
from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
from transformers import (
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    TrainingArguments,
    is_torch_tpu_available,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version

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:
    """
    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."},
    )
    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]`."
        },
    )
    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.
    """

    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 and masking. 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 span masked language modeling loss"}
    )
    mean_noise_span_length: float = field(
        default=3.0,
        metadata={"help": "Mean span length of masked tokens"},
    )

    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."


def compute_input_and_target_lengths(inputs_length, noise_density, mean_noise_span_length):
    """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ .

    Training parameters to avoid padding with random_spans_noise_mask.
    When training a model with random_spans_noise_mask, we would like to set the other
    training hyperparmeters in a way that avoids padding.
    This function helps us compute these hyperparameters.
    We assume that each noise span in the input is replaced by extra_tokens_per_span_inputs sentinel tokens,
    and each non-noise span in the targets is replaced by extra_tokens_per_span_targets sentinel tokens.
    This function tells us the required number of tokens in the raw example (for split_tokens())
    as well as the length of the encoded targets. Note that this function assumes
    the inputs and targets will have EOS appended and includes that in the reported length.

    Args:
        inputs_length: an integer - desired length of the tokenized inputs sequence
        noise_density: a float
        mean_noise_span_length: a float
    Returns:
        tokens_length: length of original text in tokens
        targets_length: an integer - length in tokens of encoded targets sequence
    """

    def _tokens_length_to_inputs_length_targets_length(tokens_length):
        num_noise_tokens = int(round(tokens_length * noise_density))
        num_nonnoise_tokens = tokens_length - num_noise_tokens
        num_noise_spans = int(round(num_noise_tokens / mean_noise_span_length))
        # inputs contain all nonnoise tokens, sentinels for all noise spans
        # and one EOS token.
        _input_length = num_nonnoise_tokens + num_noise_spans + 1
        _output_length = num_noise_tokens + num_noise_spans + 1
        return _input_length, _output_length

    tokens_length = inputs_length

    while _tokens_length_to_inputs_length_targets_length(tokens_length + 1)[0] <= inputs_length:
        tokens_length += 1

    inputs_length, targets_length = _tokens_length_to_inputs_length_targets_length(
        tokens_length)

    # minor hack to get the targets length to be equal to inputs length
    # which is more likely to have been set to a nice round number.
    if noise_density == 0.5 and targets_length > inputs_length:
        tokens_length -= 1
        targets_length -= 1
    return tokens_length, targets_length


@flax.struct.dataclass
# class FlaxDataCollatorForT5MLM:
class DataCollatorForT5MLM:
    """
    Data collator used for T5 span-masked language modeling.
    It is made sure that after masking the inputs are of length `data_args.max_seq_length` and targets are also of fixed length.
    For more information on how T5 span-masked language modeling works, one can take a look
    at the `official paper <https://arxiv.org/pdf/1910.10683.pdf>`__
    or the `official code for preprocessing <https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py>`__ .

    Args:
        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
            The tokenizer used for encoding the data.
        noise_density (:obj:`float`):
            The probability with which to (randomly) mask tokens in the input.
        mean_noise_span_length (:obj:`float`):
            The average span length of the masked tokens.
        input_length (:obj:`int`):
            The expected input length after masking.
        target_length (:obj:`int`):
            The expected target length after masking.
        pad_token_id: (:obj:`int`):
            The pad token id of the model
        decoder_start_token_id: (:obj:`int):
            The decoder start token id of the model
    """

    tokenizer: PreTrainedTokenizerBase
    noise_density: float
    mean_noise_span_length: float
    input_length: int
    target_length: int
    pad_token_id: int
    decoder_start_token_id: int

    def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:

        # convert list to dict and tensorize input
        batch = BatchEncoding(
            {k: np.array([examples[i][k] for i in range(len(examples))])
             for k, v in examples[0].items()}
        )

        input_ids = batch["input_ids"]
        batch_size, expandend_input_length = input_ids.shape

        mask_indices = np.asarray([self.random_spans_noise_mask(
            expandend_input_length) for i in range(batch_size)])
        labels_mask = ~mask_indices

        input_ids_sentinel = self.create_sentinel_ids(
            mask_indices.astype(np.int8))
        labels_sentinel = self.create_sentinel_ids(labels_mask.astype(np.int8))

        batch["input_ids"] = self.filter_input_ids(
            input_ids, input_ids_sentinel)
        batch["labels"] = self.filter_input_ids(input_ids, labels_sentinel)

        if batch["input_ids"].shape[-1] != self.input_length:
            raise ValueError(
                f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but should be {self.target_length}."
            )

        if batch["labels"].shape[-1] != self.target_length:
            raise ValueError(
                f"`labels` are incorrectly preprocessed. `labels` length is {batch['labels'].shape[-1]}, but should be {self.target_length}."
            )

        # to check that tokens are correctly preprocessed, one can run `self.tokenizer.batch_decode(input_ids)` and `self.tokenizer.batch_decode(labels)` here...
        batch["decoder_input_ids"] = shift_tokens_right(
            batch["labels"], self.pad_token_id, self.decoder_start_token_id
        )
        batch['decoder_input_ids'] = torch.tensor(batch['decoder_input_ids'])
        # print(batch, flush=True)
        return batch

    def create_sentinel_ids(self, mask_indices):
        """
        Sentinel ids creation given the indices that should be masked.
        The start indices of each mask are replaced by the sentinel ids in increasing
        order. Consecutive mask indices to be deleted are replaced with `-1`.
        """
        start_indices = mask_indices - \
            np.roll(mask_indices, 1, axis=-1) * mask_indices
        start_indices[:, 0] = mask_indices[:, 0]

        sentinel_ids = np.where(start_indices != 0, np.cumsum(
            start_indices, axis=-1), start_indices)
        sentinel_ids = np.where(
            sentinel_ids != 0, (len(self.tokenizer) - sentinel_ids), 0)
        sentinel_ids -= mask_indices - start_indices
        # sentinel_ids = torch.tensor(sentinel_ids)

        return sentinel_ids

    def filter_input_ids(self, input_ids, sentinel_ids):
        """
        Puts sentinel mask on `input_ids` and fuse consecutive mask tokens into a single mask token by deleting.
        This will reduce the sequence length from `expanded_inputs_length` to `input_length`.
        """
        batch_size = input_ids.shape[0]

        input_ids_full = np.where(sentinel_ids != 0, sentinel_ids, input_ids)
        # input_ids tokens and sentinel tokens are >= 0, tokens < 0 are
        # masked tokens coming after sentinel tokens and should be removed
        input_ids = input_ids_full[input_ids_full >=
                                   0].reshape((batch_size, -1))
        input_ids = np.concatenate(
            [input_ids, np.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=np.int32)], axis=-1
        )
        input_ids = torch.tensor(input_ids)
        return input_ids

    def random_spans_noise_mask(self, length):
        """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .

        Noise mask consisting of random spans of noise tokens.
        The number of noise tokens and the number of noise spans and non-noise spans
        are determined deterministically as follows:
        num_noise_tokens = round(length * noise_density)
        num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length)
        Spans alternate between non-noise and noise, beginning with non-noise.
        Subject to the above restrictions, all masks are equally likely.

        Args:
            length: an int32 scalar (length of the incoming token sequence)
            noise_density: a float - approximate density of output mask
            mean_noise_span_length: a number

        Returns:
            a boolean tensor with shape [length]
        """

        orig_length = length

        num_noise_tokens = int(np.round(length * self.noise_density))
        # avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
        num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
        num_noise_spans = int(
            np.round(num_noise_tokens / self.mean_noise_span_length))

        # avoid degeneracy by ensuring positive number of noise spans
        num_noise_spans = max(num_noise_spans, 1)
        num_nonnoise_tokens = length - num_noise_tokens

        # pick the lengths of the noise spans and the non-noise spans
        def _random_segmentation(num_items, num_segments):
            """Partition a sequence of items randomly into non-empty segments.
            Args:
                num_items: an integer scalar > 0
                num_segments: an integer scalar in [1, num_items]
            Returns:
                a Tensor with shape [num_segments] containing positive integers that add
                up to num_items
            """
            mask_indices = np.arange(num_items - 1) < (num_segments - 1)
            np.random.shuffle(mask_indices)
            first_in_segment = np.pad(mask_indices, [[1, 0]])
            segment_id = np.cumsum(first_in_segment)
            # count length of sub segments assuming that list is sorted
            _, segment_length = np.unique(segment_id, return_counts=True)
            return segment_length

        noise_span_lengths = _random_segmentation(
            num_noise_tokens, num_noise_spans)
        nonnoise_span_lengths = _random_segmentation(
            num_nonnoise_tokens, num_noise_spans)

        interleaved_span_lengths = np.reshape(
            np.stack([nonnoise_span_lengths, noise_span_lengths],
                     axis=1), [num_noise_spans * 2]
        )
        span_starts = np.cumsum(interleaved_span_lengths)[:-1]
        span_start_indicator = np.zeros((length,), dtype=np.int8)
        span_start_indicator[span_starts] = True
        span_num = np.cumsum(span_start_indicator)
        is_noise = np.equal(span_num % 2, 1)
        # is_noise = torch.tensor(is_noise[:orig_length])
        return is_noise[:orig_length]
        # return is_noise


def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.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 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)


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if (
        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."
        )
    
        # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )


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

    # Log on each process the small summary:
    logger = logging.getLogger(__name__)

    # Set the verbosity to info of the Transformers logger (on main process only):
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Handle the repository creation
    if training_args.push_to_hub:
        from transformers.utils import get_full_repo_name
        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 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).
    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,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )

        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}%]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
            )
            datasets["train"] = load_dataset(
                data_args.dataset_name,
                data_args.dataset_config_name,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
            )
    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,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )

        if "validation" not in datasets.keys():
            datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
            )
            datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                use_auth_token=True if model_args.use_auth_token else None,
            )
    # 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
    print('datasets',datasets['train'], flush=True) # 95% of all training dataset.

    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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    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,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    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."
        )

    if model_args.config_name:
        config = T5Config.from_pretrained(
            model_args.config_name,
            cache_dir=model_args.cache_dir,
            vocab_size=len(tokenizer),
            use_auth_token=True if model_args.use_auth_token else None,
        )
    elif model_args.model_name_or_path:
        config = T5Config.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning(
            "You are instantiating a new config instance from scratch.")

    # * 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]

    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
    # Since we make sure that all sequences are of the same length, no attention_mask is needed.
    def tokenize_function(examples):
        return tokenizer(examples[text_column_name], return_attention_mask=False)

    tokenized_datasets = datasets.map(
        tokenize_function,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        remove_columns=column_names,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    # T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
    # To ensure that the input length is `max_seq_length`, we need to increase the maximum length
    # according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
    expanded_inputs_length, targets_length = compute_input_and_target_lengths(
        inputs_length=max_seq_length,
        noise_density=data_args.mlm_probability,
        mean_noise_span_length=data_args.mean_noise_span_length,
    )

    # Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
    def group_texts(examples):
        # Concatenate all texts.
        concatenated_examples = {
            k: list(chain(*examples[k])) for k in examples.keys()}
        total_length = len(concatenated_examples[list(examples.keys())[0]])
        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
        # customize this part to your needs.
        if total_length >= expanded_inputs_length:
            total_length = (
                total_length // expanded_inputs_length) * expanded_inputs_length
        # Split by chunks of max_len.
        result = {
            k: [t[i: i + expanded_inputs_length]
                for i in range(0, total_length, expanded_inputs_length)]
            for k, t in concatenated_examples.items()
        }
        return result

    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
    # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
    # might be slower to preprocess.
    #
    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
    tokenized_datasets = tokenized_datasets.map(
        group_texts,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    # Enable tensorboard only on the master node
    has_tensorboard = is_tensorboard_available()
    # if has_tensorboard and jax.process_index() == 0:
    if has_tensorboard:
        try:
            # from flax.metrics.tensorboard import SummaryWriter
            summary_writer = SummaryWriter(
                log_dir=Path(training_args.output_dir))
        except ImportError as ie:
            has_tensorboard = False
            logger.warning(
                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
            )
    else:
        logger.warning(
            "Unable to display metrics through TensorBoard because the package is not installed: "
            "Please run pip install tensorboard to enable."
        )

    # Initialize our training
    # rng = jax.random.PRNGKey(training_args.seed)
    # dropout_rngs = jax.random.split(rng, jax.local_device_count())

    if model_args.model_name_or_path:
        # model = FlaxT5ForConditionalGeneration.from_pretrained(
        # model = T5ForConditionalGeneration.from_pretrained(
        #     model_args.model_name_or_path,
        #     config=config,
        #     use_auth_token=True if model_args.use_auth_token else None,
        model = T5ForConditionalGeneration.from_pretrained('t5-base',cache_dir=model_args.cache_dir)
    else:
        config.vocab_size = len(tokenizer)
        # model = FlaxT5ForConditionalGeneration(
        model = T5ForConditionalGeneration(
            config,
        )

    # Data collator
    # This one will take care of randomly masking the tokens.
    # data_collator = FlaxDataCollatorForT5MLM(
    data_collator = DataCollatorForT5MLM(
        tokenizer=tokenizer,
        noise_density=data_args.mlm_probability,
        mean_noise_span_length=data_args.mean_noise_span_length,
        input_length=max_seq_length,
        target_length=targets_length,
        pad_token_id=model.config.pad_token_id,
        decoder_start_token_id=model.config.decoder_start_token_id,
    )

    # Store some constant
    num_epochs = int(training_args.num_train_epochs)
    # train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
    # eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
    train_batch_size = int(training_args.per_device_train_batch_size)
    eval_batch_size = int(training_args.per_device_eval_batch_size)

    num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
    train_dataset = tokenized_datasets["train"]
    if training_args.do_eval:
        if "validation" not in tokenized_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = tokenized_datasets["validation"]

        def preprocess_logits_for_metrics(logits, labels):
            if isinstance(logits, tuple):
                # Depending on the model and config, logits may contain extra tensors,
                # like past_key_values, but logits always come first
                logits = logits[0]
            return logits.argmax(dim=-1)

        metric = load_metric("accuracy")

        def compute_metrics(eval_preds):
            preds, labels = eval_preds
            # preds have the same shape as the labels, after the argmax(-1) has been calculated
            # by preprocess_logits_for_metrics
            labels = labels.reshape(-1)
            preds = preds.reshape(-1)
            mask = labels != -100
            labels = labels[mask]
            preds = preds[mask]
            return metric.compute(predictions=preds, references=labels)
    else:
        eval_dataset = None
    # print(data_collator(train_dataset),flush=True)

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
        preprocess_logits_for_metrics=preprocess_logits_for_metrics
        if training_args.do_eval and not is_torch_tpu_available()
        else None,
    )

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload
        metrics = train_result.metrics

        metrics["train_samples"] = len(train_dataset)

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
    
    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()
        metrics["eval_samples"] = len(eval_dataset)
        try:
            perplexity = math.exp(metrics["eval_loss"])
        except OverflowError:
            perplexity = float("inf")
        metrics["perplexity"] = perplexity

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # num_of_hosts = jax.process_count()
    # current_host_idx = jax.process_index()

    # # Create learning rate schedule
    # 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]
    # )

    # 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.
    # def decay_mask_fn(params):
    #     flat_params = traverse_util.flatten_dict(params)
    #     flat_mask = {
    #         path: (path[-1] != "bias" and path[-2:]
    #                not in [("layer_norm", "scale"), ("final_layer_norm", "scale")])
    #         for path in flat_params
    #     }
    #     return traverse_util.unflatten_dict(flat_mask)

    # # create adam optimizer
    # if training_args.adafactor:
    #     # We use the default parameters here to initialize adafactor,
    #     # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
    #     optimizer = optax.adafactor(
    #         learning_rate=linear_decay_lr_schedule_fn,
    #     )
    # else:
    #     optimizer = optax.adamw(
    #         learning_rate=linear_decay_lr_schedule_fn,
    #         b1=training_args.adam_beta1,
    #         b2=training_args.adam_beta2,
    #         weight_decay=training_args.weight_decay,
    #         mask=decay_mask_fn,
    #     )

    # # Setup train state
    # state = train_state.TrainState.create(
    #     apply_fn=model.__call__, params=model.params, tx=optimizer)

    # # Define gradient update step fn
    # 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]

    #         # compute loss
    #         loss = optax.softmax_cross_entropy(
    #             logits, onehot(labels, logits.shape[-1])).mean()

    #         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

    # # Create parallel version of the train step
    # p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))

    # # Define eval fn
    # def eval_step(params, batch):
    #     labels = batch.pop("labels")

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

    #     # compute loss
    #     loss = optax.softmax_cross_entropy(
    #         logits, onehot(labels, logits.shape[-1]))

    #     # compute accuracy
    #     accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels)

    #     # summarize metrics
    #     metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
    #     metrics = jax.lax.pmean(metrics, axis_name="batch")

    #     return metrics

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

    # # Replicate the train state on each device
    # state = jax_utils.replicate(state)

    # train_time = 0
    # epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
    # for epoch in epochs:
    #     # TODO ======================== Training ================================
    #     print('Begin training...', flush=True)
    #     train_start = time.time()
    #     train_metrics = []

    #     # Create sampling rng
    #     rng, input_rng = jax.random.split(rng)

    #     # Generate an epoch by shuffling sampling indices from the train dataset
    #     num_train_samples = len(tokenized_datasets["train"])
    #     train_samples_idx = np.random.permutation(np.arange(num_train_samples))
    #     train_batch_idx = generate_batch_splits(
    #         train_samples_idx, train_batch_size)

    #     # Gather the indexes for creating the batch and do a training step
    #     for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
    #         samples = [tokenized_datasets["train"]
    #                    [int(idx)] for idx in batch_idx]
    #         model_inputs = data_collator(samples)

    #         local_host_model_inputs = {
    #             key: np.split(model_inputs.data[key], num_of_hosts, axis=0)[
    #                 current_host_idx]
    #             for key, value in model_inputs.data.items()
    #         }

    #         # Model forward
    #         model_inputs = shard(local_host_model_inputs)
    #         state, train_metric, dropout_rngs = p_train_step(
    #             state, model_inputs, dropout_rngs)
    #         train_metrics.append(train_metric)

    #         cur_step = epoch * (num_train_samples // train_batch_size) + step

    #         if cur_step % training_args.logging_steps == 0 and cur_step > 0:
    #             # Save metrics
    #             train_metric = jax_utils.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} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
    #             )

    #             train_metrics = []

    #         if cur_step % training_args.eval_steps == 0 and cur_step > 0:
    #             # ======================== Evaluating ==============================
    #             num_eval_samples = len(tokenized_datasets["validation"])
    #             eval_samples_idx = jnp.arange(num_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)

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

    #             # get eval metrics
    #             eval_metrics = get_metrics(eval_metrics)
    #             eval_metrics = jax.tree_map(jnp.mean, eval_metrics)

    #             # Update progress bar
    #             epochs.write(
    #                 f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})")

    #             # Save metrics
    #             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:
    #             # save checkpoint after each epoch and push checkpoint to the hub
    #             if jax.process_index() == 0:
    #                 params = jax.device_get(
    #                     jax.tree_map(lambda x: x[0], 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)

    # # Eval after training
    # if training_args.do_eval:
    #     num_eval_samples = len(tokenized_datasets["validation"])
    #     eval_samples_idx = jnp.arange(num_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)

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

    #     # get eval metrics
    #     eval_metrics = get_metrics(eval_metrics)
    #     eval_metrics = jax.tree_map(
    #         lambda metric: jnp.mean(metric).item(), eval_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()
