run-qg.py

#!/usr/bin/env python
# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

import logging
import numbers
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    M2M100Tokenizer,
    MBart50Tokenizer,
    MBart50TokenizerFast,
    MBartTokenizer,
    MBartTokenizerFast,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.data.metrics.squad_metrics import compute_f1
from transformers.trainer_utils import get_last_checkpoint

import datasets
from datasets import load_dataset

logger = logging.getLogger(__name__)

# A list of all multilingual tokenizer which require src_lang and tgt_lang attributes.
MULTILINGUAL_TOKENIZERS = [
    MBartTokenizer,
    MBartTokenizerFast,
    MBart50Tokenizer,
    MBart50TokenizerFast,
    M2M100Tokenizer,
]


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

    model_name_or_path: str = field(
        metadata={
            "help": "Path to pretrained model or model identifier from huggingface.co/models"
        }
    )
    config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained config name or path if not the same as model_name"
        },
    )
    tokenizer_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained tokenizer name or path if not the same as model_name"
        },
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={
            "help": "Where to store the pretrained models downloaded from huggingface.co"
        },
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={
            "help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."
        },
    )
    model_revision: str = field(
        default="main",
        metadata={
            "help": "The specific model version to use (can be a branch name, tag name or commit id)."
        },
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
                    "with private models)."
        },
    )


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

    source_lang: str = field(
        default=None, metadata={"help": "Source language id for translation."}
    )
    target_lang: str = field(
        default=None, metadata={"help": "Target language id for translation."}
    )

    dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)."},
    )
    input_column_name: Optional[str] = field(
        default=None,
        metadata={"help": "The column name of the input in the dataset."},
    )
    target_column_name: Optional[str] = field(
        default=None,
        metadata={"help": "The column name of the target in the dataset."},
    )
    e2e: Optional[bool] = (
        field(default=False, metadata={"help": "Prepare data for e2e"}),
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The configuration name of the dataset to use (via the datasets library)."
        },
    )
    dataset_field_name: Optional[str] = field(
        default=None,
        metadata={"help": "The field name, useful when the data is stored as json."},
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input evaluation data file to evaluate the metrics (sacreblue) on "
                    "a jsonlines file."
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the metrics (sacreblue) on "
                    "a jsonlines file."
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached training and evaluation sets"},
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded."
        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded."
        },
    )
    val_max_target_length: Optional[int] = field(
        default=None,
        metadata={
            "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
                    "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                    "during ``evaluate`` and ``predict``."
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": "Whether to pad all samples to model maximum sentence length. "
                    "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                    "efficient on GPU but very bad for TPU."
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
                    "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                    "value if set."
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                    "value if set."
        },
    )
    num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                    "which is used during ``evaluate`` and ``predict``."
        },
    )
    repetition_penalty: float = field(
        default=1.0,
        metadata={
            "help": "The parameter for repetition penalty. 1.0 means no penalty. See "
                    "https://arxiv.org/abs/1909.05858 for more details."
        },
    )
    length_penalty: float = field(
        default=1.0,
        metadata={
            "help": "Exponential penalty to the length that is used with beam-based generation. "
                    "It is applied as an exponent to the sequence length, "
                    "which in turn is used to divide the score of the sequence. "
                    "Since the score is the log likelihood of the sequence (i.e. negative), length_penalty > 0.0 "
                    "promotes longer sequences, while length_penalty < 0.0 encourages shorter sequences."
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
    source_prefix: Optional[str] = field(
        default=None,
        metadata={
            "help": "A prefix to add before every source text (useful for T5 models)."
        },
    )
    forced_bos_token: Optional[str] = field(
        default=None,
        metadata={
            "help": "The token to force as the first generated token after the :obj:`decoder_start_token_id`."
                    "Useful for multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token "
                    "needs to be the target language token.(Usually it is the target language token)"
        },
    )

    def __post_init__(self):

        if self.train_file is not None:
            extension = self.train_file.split(".")[-1]
            assert extension == "json", "`train_file` should be a json file."
        if self.validation_file is not None:
            extension = self.validation_file.split(".")[-1]
            assert extension == "json", "`validation_file` should be a json file."
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length


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.

    model_args: ModelArguments
    data_args: DataTrainingArguments
    training_args: Seq2SeqTrainingArguments
    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments)
    )
    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()

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.logging.set_verbosity(log_level)
    transformers.logging.enable_default_handler()
    transformers.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}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    if data_args.source_prefix is None and model_args.model_name_or_path in [
        "t5-small",
        "t5-base",
        "t5-large",
        "t5-3b",
        "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
            "`--source_prefix 'translate English to German: ' `"
        )

    # 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(list(filter(lambda p: not p.startswith("."), 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."
            )

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

    # Get the datasets: you can either provide your own JSON 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 translation, only JSON files are supported, with one field named "translation" containing two keys for the
    # source and target languages (unless you adapt what follows).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            # field=data_args.dataset_field_name,
            cache_dir=model_args.cache_dir,
        )
    else:
        raw_datasets = load_dataset("deepset/germanquad")

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name
        else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
        num_beams=data_args.num_beams,
        max_length=data_args.max_target_length,
        repetition_penalty=data_args.repetition_penalty,
        length_penalty=data_args.length_penalty,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name
        else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer.add_special_tokens({"additional_special_tokens": ["<hl>"]})

    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
    if model.resize_token_embeddings(new_num_tokens=None).num_embeddings < len(tokenizer):
        embedding = model.resize_token_embeddings(new_num_tokens=len(tokenizer),
                                                  pad_to_multiple_of=8 if training_args.fp16 else 4)
        config.vocab_size = embedding.num_embeddings

    # Set decoder_start_token_id
    if model.config.decoder_start_token_id is None and isinstance(
            tokenizer, (MBartTokenizer, MBartTokenizerFast)
    ):
        if isinstance(tokenizer, MBartTokenizer):
            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[
                data_args.target_lang
            ]
        else:
            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(
                data_args.target_lang
            )

    if model.config.decoder_start_token_id is None:
        raise ValueError(
            "Make sure that `config.decoder_start_token_id` is correctly defined"
        )

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        column_names = raw_datasets["test"].column_names
    else:
        logger.info(
            "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
        )
        return

    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
    # ignore those attributes).
    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
        assert (
                data_args.target_lang is not None and data_args.source_lang is not None
        ), (
            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
            "--target_lang arguments."
        )

        tokenizer.src_lang = data_args.source_lang
        tokenizer.tgt_lang = data_args.target_lang

        # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
        # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
        forced_bos_token_id = (
            tokenizer.lang_code_to_id[data_args.forced_bos_token]
            if data_args.forced_bos_token is not None
            else None
        )
        model.config.forced_bos_token_id = forced_bos_token_id

    # Get the language codes for input/target.
    # source_lang = data_args.source_lang.split("_")[0]
    # target_lang = data_args.target_lang.split("_")[0]

    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(
            model, "prepare_decoder_input_ids_from_labels"
    ):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    def preprocess_function(samples):
        inputs = samples[data_args.input_column_name]
        targets = samples[data_args.target_column_name]

        inputs = [prefix + _input for _input in inputs]
        model_inputs = tokenizer(
            inputs,
            max_length=data_args.max_source_length,
            padding=padding,
            truncation=True,
        )

        # Set up the tokenizer for targets
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(
                targets, max_length=max_target_length, padding=padding, truncation=True
            )

        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            pad_token_id = tokenizer.pad_token_id

            labels["input_ids"] = [
                [
                    (token_id if token_id != pad_token_id else -100) for token_id in label
                ] for label in labels["input_ids"]
            ]

        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))
        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_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 train dataset",
            )

    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
        with training_args.main_process_first(
                desc="validation dataset map pre-processing"
        ):
            eval_dataset = eval_dataset.map(
                preprocess_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 validation dataset",
            )

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = raw_datasets["test"]
        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(
                range(data_args.max_predict_samples)
            )
        with training_args.main_process_first(
                desc="prediction dataset map pre-processing"
        ):
            predict_dataset = predict_dataset.map(
                preprocess_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 prediction dataset",
            )

    # Data collator
    label_pad_token_id = (
        -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    )
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    else:
        data_collator = DataCollatorForSeq2Seq(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )

    # Metric
    metric = evaluate.combine(["sacrebleu", "rouge", "exact_match"])

    def postprocess_text(_predictions, labels):
        _predictions = [pred.strip() for pred in _predictions]
        labels = [label.strip() for label in labels]

        return _predictions, labels

    def flatten(input_dict: dict) -> dict[str, numbers.Real]:
        def _flatten(key, value, output):
            if isinstance(value, numbers.Real):
                output[key] = round(value, 4)
            elif isinstance(value, dict):
                for dict_key, dict_value in value.items():
                    _flatten(f"{key}_{dict_key}", dict_value, output)
            elif isinstance(value, list):
                for i, list_value in enumerate(value, 1):
                    _flatten(f"{key}_{i}", list_value, output)
            else:
                raise ValueError(f"Unsupported type: {type(value)}")

        out_dict = {}
        for k, v in input_dict.items():
            _flatten(k, v, out_dict)
        return out_dict

    def compute_metrics(eval_predictions):
        _predictions, labels = eval_predictions
        if isinstance(_predictions, tuple):
            _predictions = _predictions[0]
        decoded_predictions = tokenizer.batch_decode(_predictions, skip_special_tokens=True)
        if data_args.ignore_pad_token_for_loss:
            # Replace -100 in the labels as we can't decode them.
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        # Some simple post-processing
        decoded_predictions, decoded_labels = postprocess_text(decoded_predictions, decoded_labels)

        result = metric.compute(predictions=decoded_predictions, references=decoded_labels)
        result["bleu"] = result["score"]
        del result["score"]

        prediction_lens = [
            np.count_nonzero(pred != tokenizer.pad_token_id) for pred in _predictions
        ]
        result["gen_len"] = np.mean(prediction_lens)

        # Compute F1 score
        f1_scores: list[float] = []
        for label, prediction in zip(decoded_labels, decoded_predictions):
            f1_scores.append(compute_f1(a_gold=label, a_pred=prediction))
        result["f1"] = np.mean(f1_scores)

        result = flatten(result)
        return result

    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        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.predict_with_generate
        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
        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()

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate(metric_key_prefix="eval")
        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))

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

    if training_args.do_predict:
        logger.info("*** Predict ***")

        predict_results = trainer.predict(
            predict_dataset,
            metric_key_prefix="predict"
        )
        predictions = [
            tokenizer.decode(ids, skip_special_tokens=True)
            for ids in predict_results.predictions
        ]
        with open(f"{training_args.output_dir}/predictions.txt", "w", encoding="utf-8") as f:
            f.write("\n".join(predictions))
        sources = [
            tokenizer.decode(
                list(map(lambda t: 0 if t == -100 else t, ids)),
                skip_special_tokens=True,
            )
            for ids in predict_results.label_ids
        ]
        with open(f"{training_args.output_dir}/labels.txt", "w", encoding="utf-8") as f:
            f.write("\n".join(sources))
        metrics = predict_results.metrics
        max_predict_samples = (
            data_args.max_predict_samples
            if data_args.max_predict_samples is not None
            else len(predict_dataset)
        )
        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))

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

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:
                predictions = tokenizer.batch_decode(
                    predict_results.predictions,
                    skip_special_tokens=True,
                    clean_up_tokenization_spaces=True,
                )
                predictions = [pred.strip() for pred in predictions]
                output_prediction_file = os.path.join(
                    training_args.output_dir, "generated_predictions.txt"
                )
                with open(output_prediction_file, "w", encoding="utf-8") as writer:
                    writer.write("\n".join(predictions))

    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "question generation",
    }
    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

    languages = [
        language for language in [data_args.source_lang, data_args.target_lang] if language is not None
    ]
    if len(languages) > 0:
        kwargs["language"] = languages

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)

    return results


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()