#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 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 question answering.
"""
# You can also adapt this script on your own question answering task. Pointers for this are left as comments.

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

import evaluate
import tensorflow as tf
from datasets import load_dataset
from packaging.version import parse
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizerFast,
    PushToHubCallback,
    TFAutoModelForQuestionAnswering,
    TFTrainingArguments,
    create_optimizer,
    set_seed,
)
from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry


try:
    import tf_keras as keras
except (ModuleNotFoundError, ImportError):
    import keras

    if parse(keras.__version__).major > 2:
        raise ValueError(
            "Your currently installed version of Keras is Keras 3, but this is not yet supported in "
            "Transformers. Please install the backwards-compatible tf-keras package with "
            "`pip install tf-keras`."
        )


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.41.0.dev0")

logger = logging.getLogger(__name__)


# 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": "Path to directory to store the pretrained models downloaded from huggingface.co"},
    )
    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`)."
            )
        },
    )
    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. 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.
    """

    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)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input test data file to evaluate the perplexity on (a text 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_seq_length: int = field(
        default=384,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
                " batching to the maximum length in the batch (which can be faster on GPU but will be slower on 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."
            )
        },
    )
    version_2_with_negative: bool = field(
        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
    )
    null_score_diff_threshold: float = field(
        default=0.0,
        metadata={
            "help": (
                "The threshold used to select the null answer: if the best answer has a score that is less than "
                "the score of the null answer minus this threshold, the null answer is selected for this example. "
                "Only useful when `version_2_with_negative=True`."
            )
        },
    )
    doc_stride: int = field(
        default=128,
        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
    )
    n_best_size: int = field(
        default=20,
        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
    )
    max_answer_length: int = field(
        default=30,
        metadata={
            "help": (
                "The maximum length of an answer that can be generated. This is needed because the start "
                "and end predictions are not conditioned on one another."
            )
        },
    )

    def __post_init__(self):
        if (
            self.dataset_name is None
            and self.train_file is None
            and self.validation_file is None
            and self.test_file is None
        ):
            raise ValueError("Need either a dataset name or a training/validation file/test_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.test_file is not None:
                extension = self.test_file.split(".")[-1]
                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."


# endregion


# region Helper classes
class SavePretrainedCallback(keras.callbacks.Callback):
    # Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
    # metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
    # that saves the model with this method after each epoch.
    def __init__(self, output_dir, **kwargs):
        super().__init__()
        self.output_dir = output_dir

    def on_epoch_end(self, epoch, logs=None):
        self.model.save_pretrained(self.output_dir)


# 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()

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

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

    output_dir = Path(training_args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    # endregion

    # region Checkpoints
    checkpoint = None
    if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
        if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
            checkpoint = output_dir
            logger.info(
                f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
                " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
        else:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to continue regardless."
            )
    # 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 if training_args.should_log else logging.WARN)

    # Set the verbosity to info of the Transformers logger (on main process only):
    if training_args.should_log:
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")
    # endregion

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

    # region Load Data
    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # 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.
        datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            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]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        datasets = load_dataset(
            extension,
            data_files=data_files,
            field="data",
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.
    # endregion

    # region 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,
        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=True,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    # endregion

    # region Tokenizer check: this script requires a fast tokenizer.
    if not isinstance(tokenizer, PreTrainedTokenizerFast):
        raise ValueError(
            "This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
            " https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
            " this requirement"
        )
    # endregion

    # region Preprocessing the datasets
    # Preprocessing is slightly different for training and evaluation.
    if training_args.do_train:
        column_names = datasets["train"].column_names
    elif training_args.do_eval:
        column_names = datasets["validation"].column_names
    else:
        column_names = datasets["test"].column_names
    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    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)

    if data_args.pad_to_max_length or isinstance(training_args.strategy, tf.distribute.TPUStrategy):
        logger.info("Padding all batches to max length because argument was set or we're on TPU.")
        padding = "max_length"
    else:
        padding = False

    # Training preprocessing
    def prepare_train_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            padding=padding,
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            cls_index = input_ids.index(tokenizer.cls_token_id)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples.sequence_ids(i)

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            answers = examples[answer_column_name][sample_index]
            # If no answers are given, set the cls_index as answer.
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
            else:
                # Start/end character index of the answer in the text.
                start_char = answers["answer_start"][0]
                end_char = start_char + len(answers["text"][0])

                # Start token index of the current span in the text.
                token_start_index = 0
                while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
                    token_start_index += 1

                # End token index of the current span in the text.
                token_end_index = len(input_ids) - 1
                while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
                    token_end_index -= 1

                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
                    tokenized_examples["start_positions"].append(cls_index)
                    tokenized_examples["end_positions"].append(cls_index)
                else:
                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                    # Note: we could go after the last offset if the answer is the last word (edge case).
                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                        token_start_index += 1
                    tokenized_examples["start_positions"].append(token_start_index - 1)
                    while offsets[token_end_index][1] >= end_char:
                        token_end_index -= 1
                    tokenized_examples["end_positions"].append(token_end_index + 1)

        return tokenized_examples

    processed_datasets = {}
    if training_args.do_train:
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = datasets["train"]
        if data_args.max_train_samples is not None:
            # We will select sample from whole data if argument is specified
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
        # Create train feature from dataset
        train_dataset = train_dataset.map(
            prepare_train_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_train_samples is not None:
            # Number of samples might increase during Feature Creation, We select only specified max samples
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
        processed_datasets["train"] = train_dataset

    # Validation preprocessing
    def prepare_validation_features(examples):
        # Some of the questions have lots of whitespace on the left, which is not useful and will make the
        # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
        # left whitespace
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            padding=padding,
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")

        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
        # corresponding example_id and we will store the offset mappings.
        tokenized_examples["example_id"] = []

        for i in range(len(tokenized_examples["input_ids"])):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples.sequence_ids(i)
            context_index = 1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            tokenized_examples["example_id"].append(examples["id"][sample_index])

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            tokenized_examples["offset_mapping"][i] = [
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
            ]

        return tokenized_examples

    if training_args.do_eval:
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_examples = datasets["validation"]
        if data_args.max_eval_samples is not None:
            # We will select sample from whole data
            max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
            eval_examples = eval_examples.select(range(max_eval_samples))
        # Validation Feature Creation
        eval_dataset = eval_examples.map(
            prepare_validation_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_eval_samples is not None:
            # During Feature creation dataset samples might increase, we will select required samples again
            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))
        processed_datasets["validation"] = eval_dataset

    if training_args.do_predict:
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_examples = datasets["test"]
        if data_args.max_predict_samples is not None:
            # We will select sample from whole data
            predict_examples = predict_examples.select(range(data_args.max_predict_samples))
        # Predict Feature Creation
        predict_dataset = predict_examples.map(
            prepare_validation_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_predict_samples is not None:
            # During Feature creation dataset samples might increase, we will select required samples again
            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
            predict_dataset = predict_dataset.select(range(max_predict_samples))
        processed_datasets["test"] = predict_dataset
    # endregion

    # region Metrics and Post-processing:
    def post_processing_function(examples, features, predictions, stage="eval"):
        # Post-processing: we match the start logits and end logits to answers in the original context.
        predictions = postprocess_qa_predictions(
            examples=examples,
            features=features,
            predictions=predictions,
            version_2_with_negative=data_args.version_2_with_negative,
            n_best_size=data_args.n_best_size,
            max_answer_length=data_args.max_answer_length,
            null_score_diff_threshold=data_args.null_score_diff_threshold,
            output_dir=training_args.output_dir,
            prefix=stage,
        )
        # Format the result to the format the metric expects.
        if data_args.version_2_with_negative:
            formatted_predictions = [
                {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
            ]
        else:
            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]

        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
        return EvalPrediction(predictions=formatted_predictions, label_ids=references)

    metric = evaluate.load(
        "squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir
    )

    def compute_metrics(p: EvalPrediction):
        return metric.compute(predictions=p.predictions, references=p.label_ids)

    # endregion

    with training_args.strategy.scope():
        dataset_options = tf.data.Options()
        dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
        num_replicas = training_args.strategy.num_replicas_in_sync

        # region Load model and prepare datasets
        if checkpoint is None:
            model_path = model_args.model_name_or_path
        else:
            model_path = checkpoint
        model = TFAutoModelForQuestionAnswering.from_pretrained(
            model_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,
        )
        if training_args.do_train:
            training_dataset = model.prepare_tf_dataset(
                processed_datasets["train"],
                shuffle=True,
                batch_size=training_args.per_device_train_batch_size * num_replicas,
                tokenizer=tokenizer,
            )

            training_dataset = training_dataset.with_options(dataset_options)

            num_train_steps = len(training_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

            optimizer, schedule = create_optimizer(
                init_lr=training_args.learning_rate,
                num_train_steps=len(training_dataset) * training_args.num_train_epochs,
                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,
            )

            # 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, metrics=["accuracy"])

        else:
            # Optimizer doesn't matter as it won't be used anyway
            model.compile(optimizer="sgd", jit_compile=training_args.xla, metrics=["accuracy"])
            training_dataset = None

        if training_args.do_eval:
            eval_dataset = model.prepare_tf_dataset(
                processed_datasets["validation"],
                shuffle=False,
                batch_size=training_args.per_device_train_batch_size * num_replicas,
                tokenizer=tokenizer,
            )
            eval_dataset = eval_dataset.with_options(dataset_options)
        else:
            eval_dataset = None

        if training_args.do_predict:
            predict_dataset = model.prepare_tf_dataset(
                processed_datasets["test"],
                shuffle=False,
                batch_size=training_args.per_device_eval_batch_size * num_replicas,
                tokenizer=tokenizer,
            )
            predict_dataset = predict_dataset.with_options(dataset_options)
        else:
            predict_dataset = None

        # 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:
            if data_args.dataset_name is not None:
                push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
            else:
                push_to_hub_model_id = f"{model_name}-finetuned-question-answering"

        model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
        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

        if training_args.push_to_hub:
            callbacks = [
                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,
                )
            ]
        else:
            callbacks = []
        # endregion

        # region Training and Evaluation

        if training_args.do_train:
            # Note that the validation and test datasets have been processed in a different way to the
            # training datasets in this example, and so they don't have the same label structure.
            # As such, we don't pass them directly to Keras, but instead get model predictions to evaluate
            # after training.
            model.fit(training_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks)

        if training_args.do_eval:
            logger.info("*** Evaluation ***")

            # In this example, we compute advanced metrics at the end of training, but
            # if you'd like to compute metrics every epoch that are too complex to be written as
            # standard Keras metrics, you can use our KerasMetricCallback. See
            # https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks

            eval_predictions = model.predict(eval_dataset)
            if isinstance(eval_predictions.start_logits, tf.RaggedTensor):
                # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
                # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
                # the highest probability in a sample. Instead, we use a large negative value, which ensures that the
                # padding positions are correctly masked.
                eval_start_logits = eval_predictions.start_logits.to_tensor(default_value=-1000).numpy()
                eval_end_logits = eval_predictions.end_logits.to_tensor(default_value=-1000).numpy()
            else:
                eval_start_logits = eval_predictions.start_logits
                eval_end_logits = eval_predictions.end_logits

            post_processed_eval = post_processing_function(
                datasets["validation"],
                processed_datasets["validation"],
                (eval_start_logits, eval_end_logits),
            )
            metrics = compute_metrics(post_processed_eval)
            logging.info("Evaluation metrics:")
            for metric, value in metrics.items():
                logging.info(f"{metric}: {value:.3f}")
            if training_args.output_dir 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(metrics))
        # endregion

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

            test_predictions = model.predict(predict_dataset)
            if isinstance(test_predictions.start_logits, tf.RaggedTensor):
                # If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
                # The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
                # the highest probability in a sample. Instead, we use a large negative value, which ensures that the
                # padding positions are correctly masked.
                test_start_logits = test_predictions.start_logits.to_tensor(default_value=-1000).numpy()
                test_end_logits = test_predictions.end_logits.to_tensor(default_value=-1000).numpy()
            else:
                test_start_logits = test_predictions.start_logits
                test_end_logits = test_predictions.end_logits
            post_processed_test = post_processing_function(
                datasets["test"],
                processed_datasets["test"],
                (test_start_logits, test_end_logits),
            )
            metrics = compute_metrics(post_processed_test)

            logging.info("Test metrics:")
            for metric, value in metrics.items():
                logging.info(f"{metric}: {value:.3f}")
        # endregion

    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()
