import click
import torch
import logging
import random
import numpy as np

from utils.config import Config
from utils.visualization.plot_images_grid import plot_images_grid
from baselines.kde import KDE
from datasets.main import load_dataset


################################################################################
# Settings
################################################################################
@click.command()
@click.argument(
    "dataset_name",
    type=click.Choice(
        [
            "mnist",
            "fmnist",
            "cifar10",
            "arrhythmia",
            "cardio",
            "satellite",
            "satimage-2",
            "shuttle",
            "thyroid",
        ]
    ),
)
@click.argument("xp_path", type=click.Path(exists=True))
@click.argument("data_path", type=click.Path(exists=True))
@click.option(
    "--load_config",
    type=click.Path(exists=True),
    default=None,
    help="Config JSON-file path (default: None).",
)
@click.option(
    "--load_model",
    type=click.Path(exists=True),
    default=None,
    help="Model file path (default: None).",
)
@click.option(
    "--ratio_known_normal",
    type=float,
    default=0.0,
    help="Ratio of known (labeled) normal training examples.",
)
@click.option(
    "--ratio_known_outlier",
    type=float,
    default=0.0,
    help="Ratio of known (labeled) anomalous training examples.",
)
@click.option(
    "--ratio_pollution",
    type=float,
    default=0.0,
    help="Pollution ratio of unlabeled training data with unknown (unlabeled) anomalies.",
)
@click.option(
    "--seed", type=int, default=-1, help="Set seed. If -1, use randomization."
)
@click.option(
    "--kernel",
    type=click.Choice(
        ["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"]
    ),
    default="gaussian",
    help="Kernel for the KDE",
)
@click.option(
    "--grid_search_cv",
    type=bool,
    default=True,
    help="Use sklearn GridSearchCV to determine optimal bandwidth",
)
@click.option(
    "--n_jobs_model", type=int, default=-1, help="Number of jobs for model training."
)
@click.option(
    "--hybrid",
    type=bool,
    default=False,
    help="Train KDE on features extracted from an autoencoder. If True, load_ae must be specified.",
)
@click.option(
    "--load_ae",
    type=click.Path(exists=True),
    default=None,
    help="Model file path to load autoencoder weights (default: None).",
)
@click.option(
    "--n_jobs_dataloader",
    type=int,
    default=0,
    help="Number of workers for data loading. 0 means that the data will be loaded in the main process.",
)
@click.option(
    "--normal_class",
    type=int,
    default=0,
    help="Specify the normal class of the dataset (all other classes are considered anomalous).",
)
@click.option(
    "--known_outlier_class",
    type=int,
    default=1,
    help="Specify the known outlier class of the dataset for semi-supervised anomaly detection.",
)
@click.option(
    "--n_known_outlier_classes",
    type=int,
    default=0,
    help="Number of known outlier classes."
    "If 0, no anomalies are known."
    "If 1, outlier class as specified in --known_outlier_class option."
    "If > 1, the specified number of outlier classes will be sampled at random.",
)
def main(
    dataset_name,
    xp_path,
    data_path,
    load_config,
    load_model,
    ratio_known_normal,
    ratio_known_outlier,
    ratio_pollution,
    seed,
    kernel,
    grid_search_cv,
    n_jobs_model,
    hybrid,
    load_ae,
    n_jobs_dataloader,
    normal_class,
    known_outlier_class,
    n_known_outlier_classes,
):
    """
    (Hybrid) KDE for anomaly detection.

    :arg DATASET_NAME: Name of the dataset to load.
    :arg XP_PATH: Export path for logging the experiment.
    :arg DATA_PATH: Root path of data.
    """

    # Get configuration
    cfg = Config(locals().copy())

    # Set up logging
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    )
    log_file = xp_path + "/log.txt"
    file_handler = logging.FileHandler(log_file)
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(formatter)
    logger.addHandler(file_handler)

    # Print paths
    logger.info("Log file is %s." % log_file)
    logger.info("Data path is %s." % data_path)
    logger.info("Export path is %s." % xp_path)

    # Print experimental setup
    logger.info("Dataset: %s" % dataset_name)
    logger.info("Normal class: %d" % normal_class)
    logger.info("Ratio of labeled normal train samples: %.2f" % ratio_known_normal)
    logger.info("Ratio of labeled anomalous samples: %.2f" % ratio_known_outlier)
    logger.info("Pollution ratio of unlabeled train data: %.2f" % ratio_pollution)
    if n_known_outlier_classes == 1:
        logger.info("Known anomaly class: %d" % known_outlier_class)
    else:
        logger.info("Number of known anomaly classes: %d" % n_known_outlier_classes)

    # If specified, load experiment config from JSON-file
    if load_config:
        cfg.load_config(import_json=load_config)
        logger.info("Loaded configuration from %s." % load_config)

    # Print KDE configuration
    logger.info("KDE kernel: %s" % cfg.settings["kernel"])
    logger.info(
        "Use GridSearchCV for bandwidth selection: %s" % cfg.settings["grid_search_cv"]
    )
    logger.info("Number of jobs for model training: %d" % n_jobs_model)
    logger.info("Hybrid model: %s" % cfg.settings["hybrid"])

    # Set seed
    if cfg.settings["seed"] != -1:
        random.seed(cfg.settings["seed"])
        np.random.seed(cfg.settings["seed"])
        torch.manual_seed(cfg.settings["seed"])
        torch.cuda.manual_seed(cfg.settings["seed"])
        torch.backends.cudnn.deterministic = True
        logger.info("Set seed to %d." % cfg.settings["seed"])

    # Use 'cpu' as device for KDE
    device = "cpu"
    torch.multiprocessing.set_sharing_strategy(
        "file_system"
    )  # fix multiprocessing issue for ubuntu
    logger.info("Computation device: %s" % device)
    logger.info("Number of dataloader workers: %d" % n_jobs_dataloader)

    # Load data
    dataset = load_dataset(
        dataset_name,
        data_path,
        normal_class,
        known_outlier_class,
        n_known_outlier_classes,
        ratio_known_normal,
        ratio_known_outlier,
        ratio_pollution,
        random_state=np.random.RandomState(cfg.settings["seed"]),
    )
    # Log random sample of known anomaly classes if more than 1 class
    if n_known_outlier_classes > 1:
        logger.info("Known anomaly classes: %s" % (dataset.known_outlier_classes,))

    # Initialize KDE model
    kde = KDE(
        hybrid=cfg.settings["hybrid"],
        kernel=cfg.settings["kernel"],
        n_jobs=n_jobs_model,
        seed=cfg.settings["seed"],
    )

    # If specified, load model parameters from already trained model
    if load_model:
        kde.load_model(import_path=load_model, device=device)
        logger.info("Loading model from %s." % load_model)

    # If specified, load model autoencoder weights for a hybrid approach
    if hybrid and load_ae is not None:
        kde.load_ae(dataset_name, model_path=load_ae)
        logger.info("Loaded pretrained autoencoder for features from %s." % load_ae)

    # Train model on dataset
    kde.train(
        dataset,
        device=device,
        n_jobs_dataloader=n_jobs_dataloader,
        bandwidth_GridSearchCV=cfg.settings["grid_search_cv"],
    )

    # Test model
    kde.test(dataset, device=device, n_jobs_dataloader=n_jobs_dataloader)

    # Save results and configuration
    kde.save_results(export_json=xp_path + "/results.json")
    cfg.save_config(export_json=xp_path + "/config.json")

    # Plot most anomalous and most normal test samples
    indices, labels, scores = zip(*kde.results["test_scores"])
    indices, labels, scores = np.array(indices), np.array(labels), np.array(scores)
    idx_all_sorted = indices[np.argsort(scores)]  # from lowest to highest score
    idx_normal_sorted = indices[labels == 0][
        np.argsort(scores[labels == 0])
    ]  # from lowest to highest score

    if dataset_name in ("mnist", "fmnist", "cifar10"):

        if dataset_name in ("mnist", "fmnist"):
            X_all_low = dataset.test_set.data[idx_all_sorted[:32], ...].unsqueeze(1)
            X_all_high = dataset.test_set.data[idx_all_sorted[-32:], ...].unsqueeze(1)
            X_normal_low = dataset.test_set.data[idx_normal_sorted[:32], ...].unsqueeze(
                1
            )
            X_normal_high = dataset.test_set.data[
                idx_normal_sorted[-32:], ...
            ].unsqueeze(1)

        if dataset_name == "cifar10":
            X_all_low = torch.tensor(
                np.transpose(
                    dataset.test_set.data[idx_all_sorted[:32], ...], (0, 3, 1, 2)
                )
            )
            X_all_high = torch.tensor(
                np.transpose(
                    dataset.test_set.data[idx_all_sorted[-32:], ...], (0, 3, 1, 2)
                )
            )
            X_normal_low = torch.tensor(
                np.transpose(
                    dataset.test_set.data[idx_normal_sorted[:32], ...], (0, 3, 1, 2)
                )
            )
            X_normal_high = torch.tensor(
                np.transpose(
                    dataset.test_set.data[idx_normal_sorted[-32:], ...], (0, 3, 1, 2)
                )
            )

        plot_images_grid(X_all_low, export_img=xp_path + "/all_low", padding=2)
        plot_images_grid(X_all_high, export_img=xp_path + "/all_high", padding=2)
        plot_images_grid(X_normal_low, export_img=xp_path + "/normals_low", padding=2)
        plot_images_grid(X_normal_high, export_img=xp_path + "/normals_high", padding=2)


if __name__ == "__main__":
    main()
