run_mlm_torch.py

#!/usr/bin/env python3

from torch.utils.data import DataLoader
import logging
import os
import sys
import json
import time
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, List, Optional
import torch
import transformers
import numpy as np
import spacy
import datasets
from datasets import load_dataset, load_from_disk
from tqdm import tqdm
import math
from itertools import chain
from transformers.integrations import TensorBoardCallback
from huggingface_hub import Repository
# from transformers.models.t5.modeling_flax_t5 import shift_tokens_right

from transformers.file_utils import get_full_repo_name
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoTokenizer,
    BatchEncoding,
    HfArgumentParser,
    PreTrainedTokenizerBase,
    T5Config,
    TrainingArguments,
    is_tensorboard_available,
    set_seed,
    T5Tokenizer,
    Trainer    
)

from textsettr import TextSETTR

from sklearn.metrics import accuracy_score
from datasets import load_metric
from transformers.trainer_utils import get_last_checkpoint
#import wandb

#wandb.init(project='textsettr', entity='Torchversion',  resume="allow", sync_tensorboard=True)
#logger = logging.getLogger(__name__)

@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]`."
        },
    )


@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."},
    )
    load_from: Optional[str] = field(
        default='disk',
        metadata={"help": "enable dataset load from disk"},
    )
    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"
        },
    )
    
    train_file_path: Optional[str] = field(
        default= None,
        metadata={"help": "Path for cached train dataset"},
    )
    valid_file_path: Optional[str] = field(
        default= None,
        metadata={"help": "Path for cached valid dataset"},
    )
    
    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 process_datasets(model_args, data_args, training_args, tokenizer, expanded_inputs_length, save_path):
    raw_datasets = None
    if data_args.dataset_name is not None:
        if data_args.load_from == 'disk':
            # Loading a dataset from disk
            raw_datasets = load_from_disk(
                data_args.dataset_name, data_args.dataset_config_name
            )
        else:
            # 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
            )
        if "validation" not in raw_datasets.keys():
            if data_args.load_from == 'disk':
                raw_datasets["validation"] = load_from_disk(
                    data_args.dataset_name,
                    data_args.dataset_config_name,
                )
                raw_datasets["train"] = load_from_disk(
                    data_args.dataset_name,
                    data_args.dataset_config_name,
                    split=f"train[{data_args.validation_split_percentage}%:]",
                )
            else:
                raw_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,
                )
                raw_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,
                )
    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"
        raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)

        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
        if "validation" not in raw_datasets.keys():
            raw_datasets["validation"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[:{data_args.validation_split_percentage}%]",
                cache_dir=model_args.cache_dir,
            )
            raw_datasets["train"] = load_dataset(
                extension,
                data_files=data_files,
                split=f"train[{data_args.validation_split_percentage}%:]",
                cache_dir=model_args.cache_dir,
            )


    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    else:
        column_names = raw_datasets["validation"].column_names

    ## Modified text -> review_body for amazon_us_reviews
    text_column_name = "review_body" if "review_body" in column_names else column_names[0]

    if data_args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 512:
            logger.warning(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
            )
            max_seq_length = 512
    else:
        if data_args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
        

    # TODO: Change this up
    def tokenize_function(examples):
        inputs = tokenizer(examples['inputs'], return_attention_mask=False).input_ids
        contexts = tokenizer(examples['contexts'], return_attention_mask=False).input_ids
        return {'inputs': inputs, 'contexts':contexts}

    with training_args.main_process_first(desc="dataset map tokenization"):
        tokenized_datasets = raw_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,
            desc="Running tokenizer on every text in dataset",
        )

    # 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

    with training_args.main_process_first(desc="grouping texts together"):
        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,
            desc=f"Grouping texts in chunks of {max_seq_length}",
        )

    train_dataset, eval_dataset, test_dataset = None, None, None
    if training_args.do_train:
        if "train" not in tokenized_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = tokenized_datasets["train"]
#         if data_args.max_train_samples is not None:
#             train_dataset = train_dataset.select(range(data_args.max_train_samples))

    if training_args.do_eval:
        if "validation" not in tokenized_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = tokenized_datasets["validation"]

    if training_args.do_predict:
        if "test" not in tokenized_datasets:
            raise ValueError("--do_predict requires a test dataset")
        test_dataset = tokenized_datasets["test"]
        
    # cache the dataset, so we can load it directly for training
    if training_args.do_train:
        torch.save(train_dataset, save_path+str(max_seq_length)+'train_data.pt') 
    if training_args.do_eval:
        torch.save(eval_dataset, save_path+str(max_seq_length)+'valid_data.pt')
    if training_args.do_predict:
        torch.save(test_dataset, save_path+str(max_seq_length)+'test_data.pt')

    return train_dataset, eval_dataset, test_dataset
    
    
def create_tokenizer(model_args):
    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
        )
        print("t5-tokenizer")
        print(tokenizer)
        print('****************************************')
        return tokenizer
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
        )
        print("wiki-tokenizer")
        print(tokenizer)
        print('****************************************')
        return tokenizer
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

@dataclass
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]:      
        batch = BatchEncoding( # batch.keys() = input_ids
            {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
        )
         
        input_ids = batch['inputs']
        context_ids = batch['contexts']
        batch_size, expanded_input_length = input_ids.shape
        mask_indices = np.asarray([self.random_spans_noise_mask(expanded_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))
        inputs = self.filter_input_ids(input_ids, input_ids_sentinel)
        batch["inputs"] = torch.tensor(inputs)
        batch["contexts"] = torch.tensor(context_ids)
        labels = self.filter_input_ids(input_ids, labels_sentinel)
        batch["labels"] = torch.tensor(labels)
        if batch["inputs"].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}."
            )
        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

        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 = 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
        )
        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)
        return is_noise[:orig_length]
          
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


def create_model(model_args, tokenizer):
    if model_args.config_name:
        config = T5Config.from_pretrained(
            model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
        )
    elif model_args.model_name_or_path:
        config = T5Config.from_pretrained(
            model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
        )
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")   
    config.vocab_size = len(tokenizer)
    if model_args.model_name_or_path:
        print('................  model T5 from pretrained ...........................')
        model = TextSETTR.from_pretrained(
            model_args.model_name_or_path)
    else:
        print('.........................  T5 from scratch   ............................')
        model = TextSETTR(config)
        
        
    model.resize_token_embeddings(len(tokenizer))
    print("*************************************   config ********************************")
    print(config)
    print('*******************************************************************************')
    print('*************************************model *************************************')
    print(model)
    print("********************************************************************************")
    return model


def main():
    # 1: Parse arguments
    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()
        
    

    # 2: Output directory is exist and empty
    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."
        )

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

    # Log on each process the small summary:
    logger = logging.getLogger(__name__)
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    logger.info(f"Training/evaluation parameters {training_args}")
    logger.info(f"Model arguments {model_args}")
    logger.info(f"Data arguments {data_args}")
    
    #4:  Set seed before initializing model.
    set_seed(training_args.seed)

    #5:  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."
            )




    #6:  create  tokenizer.
    tokenizer = create_tokenizer(model_args)    
    if data_args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 512:
            logger.warning(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
            )
            max_seq_length = 512
    else:
        if data_args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # 7: 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,
    )

    #8:  Get datasets if they already exists or download and prepare them for using
    train_dataset, eval_dataset = None, None
    print('data_args.train_file_path:  -------------------------------------------------->', data_args.train_file_path)
    if data_args.train_file_path != None and data_args.valid_file_path != None:
        print('loading data')
        train_dataset  = torch.load(data_args.train_file_path)
        eval_dataset = torch.load(data_args.valid_file_path)
        print('loading done')    
    elif data_args.dataset_name is not None or data_args.train_file is not None: 
        train_dataset, eval_dataset, test_dataset = process_datasets(model_args, 
                                                       data_args, 
                                                       training_args, 
                                                       tokenizer, 
                                                       expanded_inputs_length, 
                                                       "data/")        
    else:
        raise ValueError(
            "No available datasets. You need to load a cashed dataset or process a dataset."
        )  
    
    # 9: create model     
    model = create_model(model_args, tokenizer) 

    # 10: 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
    )
    # 11:  initialize the 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,
)

    # 12: 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

#         max_train_samples = (
#             data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
#         )
#         metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # the forward function automatically creates the correct decoder_input_ids
    #print('training done. See output:  ', tokenizer.decode(torch.argmax(output['logits'][0],1)))
    #print('*****************************************')
    # 13: Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate()

#         max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
#         metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
        try:
            print('calc perplexity .. ')
            perplexity = math.exp(metrics["eval_loss"])
        except OverflowError:
            perplexity = float("inf")
        metrics["eval_perplexity"] = perplexity
        print('perplexity:  ', perplexity)


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

    print('******************* Eval Done **********************')
    
    # 13: testing
    if training_args.do_predict:
        logger.info("******************************** Testing ************************************")

        test_results = trainer.predict(test_dataset,metric_key_prefix="test")

        try:
            print('calc perplexity .. ')
            perplexity = math.exp(test_results[2]["test_loss"])
        except OverflowError:
            perplexity = float("inf")
        metrics["test_perplexity"] = perplexity
        test_results_metrics = test_results[2]
        print('test perplexity:  ', type(test_results_metrics))
        metrics.update(test_results_metrics)


        trainer.log_metrics("test", test_results_metrics)
        trainer.save_metrics("test", test_results_metrics)

    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "fill-mask"}
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)
    print('******************* Test Done **********************')
    #wandb.finish()
    


if __name__ == "__main__":
    os.environ["WANDB_DISABLED"] = "True"
    main()