# coding=utf-8
""" Fine-tuning the library models for sequence classification."""


import logging
from dataclasses import dataclass, field
from typing import List, Dict, Optional

import datasets
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)

# Added packages
import os
import shutil
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, classification_report


def get_tfds(
    train_file: str,
    eval_file: str,
    test_file: str,
    tokenizer: PreTrainedTokenizer,
    label_column_id: int,
    max_seq_length: Optional[int] = None,
):
    files = {}

    if train_file is not None:
        files[datasets.Split.TRAIN] = [train_file]
    if eval_file is not None:
        files[datasets.Split.VALIDATION] = [eval_file]
    if test_file is not None:
        files[datasets.Split.TEST] = [test_file]

    ds = datasets.load_dataset("load_CSV_tab.py", data_files=files)

    features_name = list(ds[list(files.keys())[0]].features.keys())
    label_name = features_name.pop(label_column_id)
    label_list = list(set(ds[list(files.keys())[0]][label_name]))
    label_list.sort()
    label2id = {label: i for i, label in enumerate(label_list)}
    input_names = ["input_ids"] + tokenizer.model_input_names
    transformed_ds = {}

    features_name.pop(0)  # pop "index"

    if len(features_name) == 1:
        for k in files.keys():
            transformed_ds[k] = ds[k].map(
                lambda example: tokenizer.batch_encode_plus(
                    example[features_name[0]], truncation=True, max_length=max_seq_length, padding="max_length"
                ),
                batched=True,
            )
    elif len(features_name) == 2:
        for k in files.keys():
            transformed_ds[k] = ds[k].map(
                lambda example: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]]),
                    truncation=True,
                    max_length=max_seq_length,
                    padding="max_length",
                ),
                batched=True,
            )

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            d = {k: v for k, v in ex.items() if k in input_names}
            label = label2id[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            d = {k: v for k, v in ex.items() if k in input_names}
            label = label2id[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            d = {k: v for k, v in ex.items() if k in input_names}
            label = label2id[ex[label_name]]
            yield (d, label)

    train_ds = (
        tf.data.Dataset.from_generator(
            gen_train,
            ({k: tf.int32 for k in input_names}, tf.int64),
            ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
        )
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        train_ds = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

    val_ds = (
        tf.data.Dataset.from_generator(
            gen_val,
            ({k: tf.int32 for k in input_names}, tf.int64),
            ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
        )
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        val_ds = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

    test_ds = (
        tf.data.Dataset.from_generator(
            gen_test,
            ({k: tf.int32 for k in input_names}, tf.int64),
            ({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
        )
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        test_ds = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

    return train_ds, val_ds, test_ds, label2id


logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


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

    Using `HfArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """

    label_column_id: int = field(metadata={"help": "Which column contains the label"})
    train_file: str = field(default=None, metadata={"help": "The path of the training file"})
    dev_file: Optional[str] = field(default=None, metadata={"help": "The path of the development file"})
    test_file: Optional[str] = field(default=None, metadata={"help": "The path of the test file"})
    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."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )


@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"}
    )


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)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    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,
    )

    train_dataset, eval_dataset, test_ds, label2id = get_tfds(
        train_file=data_args.train_file,
        eval_file=data_args.dev_file,
        test_file=data_args.test_file,
        tokenizer=tokenizer,
        label_column_id=data_args.label_column_id,
        max_seq_length=data_args.max_seq_length,
    )

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=len(label2id),
        label2id=label2id,
        id2label={id: label for label, id in label2id.items()},
        finetuning_task="text-classification",
        cache_dir=model_args.cache_dir,
    )

    with training_args.strategy.scope():
        model = TFAutoModelForSequenceClassification.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,
        )

    # Added function
    def compute_metrics(p: EvalPrediction, labels_for_metrics: List = None) -> Dict:
        preds = np.argmax(p.predictions, axis=1)

        assert len(p.label_ids) == len(preds)

        return {
            "precision": precision_score(p.label_ids, preds, labels=labels_for_metrics, average='micro'),
            "recall": recall_score(p.label_ids, preds, labels=labels_for_metrics, average='micro'),
            "f1": f1_score(p.label_ids, preds, labels=labels_for_metrics, average='micro'),
        }

    # Initialize our Trainer
    trainer = TFTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        labels_for_metrics=[label2id[key] for key in label2id.keys() if 'false' not in key],  # added argument
    )

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

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

        result = trainer.evaluate()
        results.update(result)

        output_eval_file_time = os.path.join(training_args.output_dir, "eval_results_time.txt")
        with open(output_eval_file_time, 'a') as writer:
            writer.write(str(trainer.epoch_logging) + "\t" + str(trainer.global_step) + "\t" + str(result['eval_loss']) + "\t" + str(result['eval_precision']) + "\t" + str(result['eval_recall']) + "\t" + str(result['eval_f1']) + "\n")

        # Create a Pandas DataFrame with the results
        res = pd.read_csv(output_eval_file_time, sep='\t')
        logger.info("***** Eval results *****")
        print('\n', res)

        if os.path.isdir(os.path.join(training_args.output_dir, 'h5')):
            # Keep only the best model
            if res.eval_f1.argmax() == res.index.max():
                # if res[res.eval_f1 == res['eval_f1'].max()].index == res.index.max():
                logger.info("Last model is the best one")
                # remove entire h5 folder
                shutil.rmtree(os.path.join(training_args.output_dir, 'h5'))

            else:
                logger.info("Best model obtained during evaluations")
                # remove 'tf_model.h5' in output_dir
                os.remove(os.path.join(training_args.output_dir, 'tf_model.h5'))
                # mv 'tf_model.h5' in h5 folder into output_dir
                shutil.move(os.path.join(training_args.output_dir, 'h5', 'tf_model.h5'), os.path.join(training_args.output_dir, 'tf_model.h5'))
                # remove what's left of the h5 folder
                shutil.rmtree(os.path.join(training_args.output_dir, 'h5'))
        else:
            logger.info("No evaluations were made during training")

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

        predictions, label_ids, metrics = trainer.predict(test_ds)
        preds = np.argmax(predictions, axis=1)

        report = classification_report(label_ids, preds,
                                       labels=trainer.labels_for_metrics if trainer.labels_for_metrics else [label2id[key] for key in label2id.keys() if 'false' not in key],
                                       digits=4,
                                       output_dict=True)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")

        logger.info("***** Test results *****")
        for key, value in metrics.items():
            logger.info("  %s = %s", key, value)

        df_report = pd.DataFrame.from_dict(report, orient='index')
        df_report.to_csv(output_test_results_file, index=True, sep='\t')

    return results


if __name__ == "__main__":
    main()
