run_t5_mlm_torch.py

# This is a mix of: 
# https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py
# https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py
# Since there seems to be no way to retrain T5 without Flax
# Done by Rob van der Goot (09-2023): [email protected]

# Biggest TODO is probably dynamic masking; in the current version the data is only 
# prepared once. I also broke the tensorboard functionality (see comments).

import json
import logging
import math
import os
import sys
import time
import warnings
from dataclasses import asdict, dataclass, field

from enum import Enum
from itertools import chain
from pathlib import Path
from typing import Dict, List, Optional

from datasets import load_dataset
from huggingface_hub import Repository, create_repo
from tqdm import tqdm
from torch.utils.data import DataLoader

from accelerate import Accelerator, DistributedType
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoTokenizer,
    BatchEncoding,
    T5ForConditionalGeneration,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    T5Config,
    is_tensorboard_available,
    set_seed,
)
from transformers.utils import send_example_telemetry
from transformers import AutoModel, get_linear_schedule_with_warmup
import torch
torch.manual_seed(8446)

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

# Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
def shift_tokens_right(input_ids , pad_token_id: int, decoder_start_token_id: int) :
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = torch.zeros(input_ids.shape, dtype=input_ids.dtype)
    #input_ids = torch.tensor(input_ids)
    shifted_input_ids[:,1:] = input_ids[:,:-1]
    shifted_input_ids[:,0] = decoder_start_token_id
    #shifted_input_ids = jnp.zeros_like(input_ids)
    #shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1])
    #shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id)
    shifted_input_ids[shifted_input_ids==-100] = pad_token_id
    #shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
    return shifted_input_ids

@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: str = field(default=None, metadata={"help": "Save checkpoint every X updates steps."})
    eval_steps: int = field(default=100, 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, 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]`."
            )
        },
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
        },
    )

@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


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
    """
    def __init__(self,
        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):

        self.tokenizer = tokenizer
        self.noise_density = noise_density
        self.mean_noise_span_length = mean_noise_span_length
        self.input_length = input_length
        self.target_length = target_length
        self.pad_token_id = pad_token_id
        self.decoder_start_token_id = decoder_start_token_id


    def __call__(self, examples: List[Dict[str, list]]) -> BatchEncoding:
        # convert list to dict and tensorize input
        input_ids = [examples[i]['input_ids'] for i in range(len(examples))]
        max_len = max([len(x) for x in input_ids])
        # could definitely be done neater
        for rowIdx in range(len(input_ids)):
            while len(input_ids[rowIdx]) != max_len:
                input_ids[rowIdx].append(self.pad_token_id)
        batch1 = {'input_ids': input_ids}
        batch1['input_ids'] = torch.tensor(batch1['input_ids'])
        batch = BatchEncoding(batch1)
            #{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 = torch.stack([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)
        labels_sentinel = self.create_sentinel_ids(labels_mask)

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

        self.input_length
        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"
                f" should be {self.input_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"
                f" {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
        )
        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`.
        """
        mask_indices = mask_indices.type(torch.int8)
        start_indices = mask_indices - torch.roll(mask_indices, 1, dims=-1) * mask_indices
        start_indices[:, 0] = mask_indices[:, 0]

        #sentinel_ids = start_indices
        sentinel_ids = torch.where(start_indices!=0, torch.cumsum(start_indices, dim=-1), start_indices)
        #sentinel_ids[start_indices != 0] = torch.cumsum(start_indices, dim=-1)#, start_indices)
        sentinel_ids = torch.where(sentinel_ids != 0,  (len(self.tokenizer) - sentinel_ids), 0)
        
        sentinel_ids -= mask_indices - start_indices

        #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)
        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 = torch.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 = torch.concat(
            [input_ids, torch.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=torch.int32)], dim=-1
        )
        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 = round(length * self.noise_density)
        num_nonnoise_tokens = length - num_noise_tokens
        # avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
        num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
        # num_noise_tokens should be less than num_noise_tokens and num_nonnoise_tokens
        num_noise_spans = round(min(num_noise_tokens, num_nonnoise_tokens) / self.mean_noise_span_length)

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

        # 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 = torch.arange(num_items - 1) < (num_segments - 1)
            # https://discuss.pytorch.org/t/shuffling-a-tensor/25422/3
            #np.random.shuffle(mask_indices)
            idx = torch.randperm(mask_indices.nelement())
            mask_indices = mask_indices.view(-1)[idx].view(mask_indices.size())
            
            first_in_segment = torch.cat([torch.tensor([False]), mask_indices])
            segment_id = torch.cumsum(first_in_segment, dim=0)
            # count length of sub segments assuming that list is sorted
            _, segment_length = torch.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 = torch.reshape(
            torch.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
        )
        span_starts = torch.cumsum(interleaved_span_lengths, dim=0)[:-1]
        span_start_indicator = torch.zeros((length,), dtype=torch.int8)
        span_start_indicator[span_starts] = True
        span_num = torch.cumsum(span_start_indicator, dim=0)
        is_noise = span_num % 2 == 1

        return is_noise[:orig_length]


def generate_batch_splits(samples_idx: list, batch_size: int, drop_last=True) -> list:
    """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
    the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
    num_samples = len(samples_idx)
    if drop_last:
        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
        samples_idx = samples_idx.reshape((sections_split, batch_size))
    else:
        sections_split = math.ceil(num_samples / batch_size)
        samples_idx = torch.split(samples_idx, sections_split)
    return samples_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()

    accelerator = Accelerator()

    if model_args.use_auth_token is not None:
        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    #send_example_telemetry("run_t5_mlm", model_args, data_args, framework="flax")

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty."
            "Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        level=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:
        # Retrieve of infer repo_name
        repo_name = training_args.hub_model_id
        if repo_name is None:
            repo_name = Path(training_args.output_dir).absolute().name
        # Create repo and retrieve repo_id
        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
        # Clone repo locally
        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)

    # Get the datasets: you can either provide your own CSV/JSON/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,
            token=model_args.token,
        )

        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,
                token=model_args.token,
            )
            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,
                token=model_args.token,
            )
    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,
            token=model_args.token,
        )

        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,
                token=model_args.token,
            )
            datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
    # 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

    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,
            token=model_args.token,
        )
    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,
            token=model_args.token,
        )
    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),
            token=model_args.token,
        )
    elif model_args.model_name_or_path:
        config = T5Config.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    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:
    #    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."
#        )

    if model_args.model_name_or_path:
        model = T5ForConditionalGeneration.from_pretrained(
            model_args.model_name_or_path,
            config=config,
            #seed=training_args.seed,
            token=model_args.token,
        )
    else:
        config.vocab_size = len(tokenizer)
        model = T5ForConditionalGeneration(
            config,
            seed=training_args.seed,
        )

    # Data collator
    # This one will take care of randomly masking the tokens.
    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,
    )

    train_dataset = tokenized_datasets["train"]
    eval_dataset = tokenized_datasets["validation"]
    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=data_collator, batch_size=training_args.per_device_train_batch_size
    )
    eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=training_args.per_device_eval_batch_size)

    # 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()
    per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
    eval_batch_size = per_device_eval_batch_size #* jax.device_count()

    num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs


    # adam optimizer
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": training_args.weight_decay,
        },
        {
            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
            "weight_decay": 0.0,
        },
    ]
    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=training_args.learning_rate, betas=(training_args.adam_beta1, training_args.adam_beta2), eps=training_args.adam_epsilon)

    # scheduler
    lr_scheduler = get_linear_schedule_with_warmup(
        optimizer=optimizer,
        num_warmup_steps= training_args.warmup_steps, #* args.gradient_accumulation_steps,
        num_training_steps=num_train_steps
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
    )

    # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
    if accelerator.distributed_type == DistributedType.TPU:
        model.tie_weights()

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader))# / args.gradient_accumulation_steps)

    #?
    #num_train_epochs = math.ceil(num_train_steps / num_update_steps_per_epoch)

    # Figure out how many steps we should save the Accelerator states
    checkpointing_steps = training_args.save_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)
    # Train!
    total_batch_size = training_args.per_device_train_batch_size * accelerator.num_processes #* args.gradient_accumulation_steps
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(num_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 0

    #print(training_args.num_train_epochs)
    for epoch in range(starting_epoch, int(training_args.num_train_epochs)):
        model.train()
        active_dataloader = train_dataloader
        for step, batch in enumerate(active_dataloader):
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                # We keep track of the loss at each epoch

                accelerator.backward(loss)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                completed_steps += 1

            if isinstance(checkpointing_steps, int):
                if completed_steps % checkpointing_steps == 0:
                    output_dir = f"step_{completed_steps }"
                    if training_args.output_dir is not None:
                        output_dir = os.path.join(training_args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

            if completed_steps >= num_train_steps:
                break

            if step % training_args.eval_steps == 0 and step > 0:
                model.eval()
                losses = []
                for dev_step, batch in enumerate(tqdm(eval_dataloader, desc="Evaluating ...", position=2)):
                    with torch.no_grad():
                        outputs = model(**batch)

                    loss = outputs.loss
                    losses.append(accelerator.gather_for_metrics(loss.repeat(training_args.per_device_eval_batch_size)))

                losses = torch.cat(losses)
                try:
                    eval_loss = torch.mean(losses)
                    perplexity = math.exp(eval_loss)
                except OverflowError:
                    perplexity = float("inf")

                logger.info(f"step {step}: perplexity: {perplexity}")

        model.eval()
        losses = []
        for step, batch in enumerate(eval_dataloader):
            with torch.no_grad():
                outputs = model(**batch)

            loss = outputs.loss
            losses.append(accelerator.gather_for_metrics(loss.repeat(training_args.per_device_eval_batch_size)))

        losses = torch.cat(losses)
        try:
            eval_loss = torch.mean(losses)
            perplexity = math.exp(eval_loss)
        except OverflowError:
            perplexity = float("inf")

        logger.info(f"epoch {epoch}: perplexity: {perplexity}")

        if training_args.push_to_hub and epoch < training_args.num_train_epochs - 1:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                training_args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                tokenizer.save_pretrained(training_args.output_dir)
                repo.push_to_hub(
                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
                )

        if training_args.save_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if training_args.output_dir is not None:
                output_dir = os.path.join(training_args.output_dir, output_dir)
            accelerator.save_state(output_dir)

    if training_args.output_dir is not None:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(
            training_args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
        )
        if accelerator.is_main_process:
            tokenizer.save_pretrained(training_args.output_dir)
            if training_args.push_to_hub:
                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)

            with open(os.path.join(training_args.output_dir, "all_results.json"), "w") as f:
                json.dump({"perplexity": perplexity}, f)
        

    
    
if __name__ == "__main__":
    main()