#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace 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 translation.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

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

import datasets
import evaluate
import numpy as np
import tensorflow as tf
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    KerasMetricCallback,
    M2M100Tokenizer,
    MBart50Tokenizer,
    MBart50TokenizerFast,
    MBartTokenizer,
    MBartTokenizerFast,
    PushToHubCallback,
    TFAutoModelForSeq2SeqLM,
    TFTrainingArguments,
    create_optimizer,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# region Dependencies and constants
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.49.0")

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

logger = logging.getLogger(__name__)
MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]
# endregion


# region Arguments
@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)."},
    )
    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`)."
            )
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to trust the execution of code from datasets/models defined on the Hub."
                " This option should only be set to `True` for repositories you trust and in which you have read the"
                " code, as it will execute code present on the Hub on your local machine."
            )
        },
    )


@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)."}
    )
    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 jsonlines or csv file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
            )
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv 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=1,
        metadata={
            "help": (
                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
        },
    )
    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.dataset_name is None and self.train_file is None and self.validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length


# endregion


def main():
    # region Argument parsing
    # 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, TFTrainingArguments))
    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()

    # 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_translation", model_args, data_args, framework="tensorflow")
    # endregion

    # region Logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO)
    datasets.utils.logging.set_verbosity(logging.INFO)
    transformers.utils.logging.set_verbosity(logging.INFO)

    # Log on each process the small summary:
    logger.info(f"Training/evaluation parameters {training_args}")
    # endregion

    # region 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."
            )
    # endregion

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

    # region Load datasets
    # Get the datasets: you can either provide your own CSV/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 CSV/JSON files this script will use the first column for the full texts and the second column for the
    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            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
    # endregion

    # region Load model config 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,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    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,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

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

    # region Dataset preprocessing
    # 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
    else:
        logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.")
        return

    column_names = raw_datasets["train"].column_names

    # 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
        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
        )

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

    padding = "max_length" if data_args.pad_to_max_length else False

    # 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

    def preprocess_function(examples):
        inputs = [ex[source_lang] for ex in examples["translation"]]
        targets = [ex[target_lang] for ex in examples["translation"]]
        inputs = [prefix + inp for inp in inputs]
        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)

        # Tokenize targets with the `text_target` keyword argument
        labels = tokenizer(text_target=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:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l 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:
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
        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",
        )
    else:
        train_dataset = None

    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:
            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))
        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",
        )
    else:
        eval_dataset = None
    # endregion

    with training_args.strategy.scope():
        # region Prepare model
        model = TFAutoModelForSeq2SeqLM.from_pretrained(
            model_args.model_name_or_path,
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )

        # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
        # on a small vocab and want a smaller embedding size, remove this test.
        embeddings = model.get_input_embeddings()

        # Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings.
        #       As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and
        #       the weights will always be in embeddings.embeddings.
        if hasattr(embeddings, "embeddings"):
            embedding_size = embeddings.embeddings.shape[0]
        else:
            embedding_size = embeddings.weight.shape[0]
        if len(tokenizer) > embedding_size:
            model.resize_token_embeddings(len(tokenizer))

        if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
            model.config.forced_bos_token_id = forced_bos_token_id
        # endregion

        # region Set decoder_start_token_id
        if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
            assert (
                data_args.target_lang is not None and data_args.source_lang is not None
            ), "mBart requires --target_lang and --source_lang"
            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")
        # endregion

        # region Prepare TF Dataset objects
        label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
        data_collator = DataCollatorForSeq2Seq(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=64,  # Reduce the number of unique shapes for XLA, especially for generation
            return_tensors="np",
        )
        num_replicas = training_args.strategy.num_replicas_in_sync
        total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
        total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas

        dataset_options = tf.data.Options()
        dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF

        # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
        # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
        # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
        # yourself if you use this method, whereas they are automatically inferred from the model input names when
        # using model.prepare_tf_dataset()
        # For more info see the docs:
        # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
        # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset

        tf_train_dataset = model.prepare_tf_dataset(
            train_dataset,
            collate_fn=data_collator,
            batch_size=total_train_batch_size,
            shuffle=True,
        ).with_options(dataset_options)
        tf_eval_dataset = model.prepare_tf_dataset(
            eval_dataset, collate_fn=data_collator, batch_size=total_eval_batch_size, shuffle=False
        ).with_options(dataset_options)
        # endregion

        # region Optimizer and LR scheduling
        num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs)
        if training_args.warmup_steps > 0:
            num_warmup_steps = training_args.warmup_steps
        elif training_args.warmup_ratio > 0:
            num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
        else:
            num_warmup_steps = 0
        if training_args.do_train:
            optimizer, lr_schedule = create_optimizer(
                init_lr=training_args.learning_rate,
                num_train_steps=num_train_steps,
                num_warmup_steps=num_warmup_steps,
                adam_beta1=training_args.adam_beta1,
                adam_beta2=training_args.adam_beta2,
                adam_epsilon=training_args.adam_epsilon,
                weight_decay_rate=training_args.weight_decay,
                adam_global_clipnorm=training_args.max_grad_norm,
            )
        else:
            optimizer = "sgd"  # Just write anything because we won't be using it
        # endregion

        # region Metric and postprocessing
        if training_args.do_eval:
            metric = evaluate.load("sacrebleu", cache_dir=model_args.cache_dir)

            if data_args.val_max_target_length is None:
                data_args.val_max_target_length = data_args.max_target_length

            gen_kwargs = {
                "max_length": data_args.val_max_target_length,
                "num_beams": data_args.num_beams,
                "no_repeat_ngram_size": 0,  # Not supported under XLA right now, and some models set it by default
            }

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

                return preds, labels

            def compute_metrics(preds):
                predictions, labels = preds
                if isinstance(predictions, tuple):
                    predictions = predictions[0]
                decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
                labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
                decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
                decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
                metrics = metric.compute(predictions=decoded_preds, references=decoded_labels)
                return {"bleu": metrics["score"]}

            # The KerasMetricCallback allows metrics that are too complex to write as standard Keras metrics
            # to be computed each epoch. Any Python code can be included in the metric_fn. This is especially
            # useful for metrics like BLEU and ROUGE that perform string comparisons on decoded model outputs.
            # For more information, see the docs at
            # https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.KerasMetricCallback

            metric_callback = KerasMetricCallback(
                metric_fn=compute_metrics,
                eval_dataset=tf_eval_dataset,
                predict_with_generate=True,
                use_xla_generation=True,
                generate_kwargs=gen_kwargs,
            )
            callbacks = [metric_callback]
        else:
            callbacks = []

        # endregion

        # region Preparing push_to_hub and model card
        push_to_hub_model_id = training_args.push_to_hub_model_id
        model_name = model_args.model_name_or_path.split("/")[-1]
        if not push_to_hub_model_id:
            push_to_hub_model_id = f"{model_name}-finetuned-{data_args.source_lang}-{data_args.target_lang}"

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

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

        if training_args.push_to_hub:
            # Because this training can be quite long, we save once per epoch.
            callbacks.append(
                PushToHubCallback(
                    output_dir=training_args.output_dir,
                    hub_model_id=push_to_hub_model_id,
                    hub_token=training_args.push_to_hub_token,
                    tokenizer=tokenizer,
                    **model_card_kwargs,
                )
            )
        # endregion

        # region Training
        eval_metrics = None
        # Transformers models compute the right loss for their task by default when labels are passed, and will
        # use this for training unless you specify your own loss function in compile().
        model.compile(optimizer=optimizer, jit_compile=training_args.xla)

        if training_args.do_train:
            logger.info("***** Running training *****")
            logger.info(f"  Num examples = {len(train_dataset)}")
            logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
            logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
            logger.info(f"  Total train batch size = {total_train_batch_size}")
            logger.info(f"  Total optimization steps = {num_train_steps}")

            if training_args.xla and not data_args.pad_to_max_length:
                logger.warning(
                    "XLA training may be slow at first when --pad_to_max_length is not set "
                    "until all possible shapes have been compiled."
                )

            history = model.fit(tf_train_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks)
            eval_metrics = {key: val[-1] for key, val in history.history.items()}
        # endregion

        # region Validation
        if training_args.do_eval and not training_args.do_train:
            # Compiling generation with XLA yields enormous speedups, see https://huggingface.co/blog/tf-xla-generate
            @tf.function(jit_compile=True)
            def generate(**kwargs):
                return model.generate(**kwargs)

            if training_args.do_eval:
                logger.info("Evaluation...")
                for batch, labels in tf_eval_dataset:
                    batch.update(gen_kwargs)
                    generated_tokens = generate(**batch)
                    if isinstance(generated_tokens, tuple):
                        generated_tokens = generated_tokens[0]
                    decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
                    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
                    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
                    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

                    metric.add_batch(predictions=decoded_preds, references=decoded_labels)

                eval_metrics = metric.compute()
                logger.info({"bleu": eval_metrics["score"]})
        # endregion

        if training_args.output_dir is not None and eval_metrics is not None:
            output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
            with open(output_eval_file, "w") as writer:
                writer.write(json.dumps(eval_metrics))

        if training_args.output_dir is not None and not training_args.push_to_hub:
            # If we're not pushing to hub, at least save a local copy when we're done
            model.save_pretrained(training_args.output_dir)


if __name__ == "__main__":
    main()
