# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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."""


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

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    HfArgumentParser,
    TFAutoModelForQuestionAnswering,
    TFTrainer,
    TFTrainingArguments,
    squad_convert_examples_to_features,
)
from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor


logger = logging.getLogger(__name__)


@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"}
    )
    use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )


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

    data_dir: Optional[str] = field(
        default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
    )
    use_tfds: Optional[bool] = field(default=True, metadata={"help": "If TFDS should be used or not."})
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    doc_stride: int = field(
        default=128,
        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
    )
    max_query_length: int = field(
        default=64,
        metadata={
            "help": "The maximum number of tokens for the question. Questions longer than this will "
            "be truncated to this length."
        },
    )
    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."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    version_2_with_negative: bool = field(
        default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
    )
    null_score_diff_threshold: float = field(
        default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
    )
    n_best_size: int = field(
        default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
    )
    lang_id: int = field(
        default=0,
        metadata={
            "help": "language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
        },
    )


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

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

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(
        "n_replicas: %s, distributed training: %s, 16-bits training: %s",
        training_args.n_replicas,
        bool(training_args.n_replicas > 1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Prepare Question-Answering task
    # 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,
    )
    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,
    )

    with training_args.strategy.scope():
        model = TFAutoModelForQuestionAnswering.from_pretrained(
            model_args.model_name_or_path,
            from_pt=bool(".bin" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )

    # Get datasets
    if data_args.use_tfds:
        if data_args.version_2_with_negative:
            logger.warn("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")

        try:
            import tensorflow_datasets as tfds
        except ImportError:
            raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")

        tfds_examples = tfds.load("squad", data_dir=data_args.data_dir)
        train_examples = (
            SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
            if training_args.do_train
            else None
        )
        eval_examples = (
            SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=True)
            if training_args.do_eval
            else None
        )
    else:
        processor = SquadV2Processor() if data_args.version_2_with_negative else SquadV1Processor()
        train_examples = processor.get_train_examples(data_args.data_dir) if training_args.do_train else None
        eval_examples = processor.get_dev_examples(data_args.data_dir) if training_args.do_eval else None

    train_dataset = (
        squad_convert_examples_to_features(
            examples=train_examples,
            tokenizer=tokenizer,
            max_seq_length=data_args.max_seq_length,
            doc_stride=data_args.doc_stride,
            max_query_length=data_args.max_query_length,
            is_training=True,
            return_dataset="tf",
        )
        if training_args.do_train
        else None
    )

    train_dataset = train_dataset.apply(tf.data.experimental.assert_cardinality(len(train_examples)))

    eval_dataset = (
        squad_convert_examples_to_features(
            examples=eval_examples,
            tokenizer=tokenizer,
            max_seq_length=data_args.max_seq_length,
            doc_stride=data_args.doc_stride,
            max_query_length=data_args.max_query_length,
            is_training=False,
            return_dataset="tf",
        )
        if training_args.do_eval
        else None
    )

    eval_dataset = eval_dataset.apply(tf.data.experimental.assert_cardinality(len(eval_examples)))

    # Initialize our Trainer
    trainer = TFTrainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset,)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir)


if __name__ == "__main__":
    main()
