import os
import numpy as np
import tensorflow as tf
from scipy.io import wavfile
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from sklearn.metrics import accuracy_score, recall_score, matthews_corrcoef
from sklearn.metrics import precision_score, f1_score, confusion_matrix
from sklearn.metrics._plot.confusion_matrix import ConfusionMatrixDisplay
from python_speech_features import logfbank
from parameters import *


def getDataset(df, batch_size, cache_file=None, shuffle=True, parse_param=PARSE_PARAMS, scale=False):
    """
    Creates a Tensorflow Dataset containing filterbanks, labels
    :param df: Dataframe with filenames and labels
    :param batch_size: Batch size of the input
    :param cache_file: Whether to cache the dataset during run
    :param shuffle: Whether to shuffle the dataset
    :param parse_param: Window parameters
    :param scale: Whether to scale filterbank levels
    :return: TF Dataset, Steps per epoch
    """

    data = tf.data.Dataset.from_tensor_slices((df["files"].tolist(), df["labels"].tolist()))

    data = data.map(
        lambda filename, label: tuple(
            tf.py_function(_parse_fn, inp=[filename, label, parse_param, scale], Tout=[tf.float32, tf.int32])
        ),
        num_parallel_calls=os.cpu_count(),
    )

    if cache_file:
        data = data.cache("../input/" + cache_file)

    if shuffle:
        data = data.shuffle(buffer_size=df.shape[0])

    data = data.batch(batch_size).prefetch(buffer_size=1)
    steps = df.shape[0] // batch_size

    return data, steps


def _loadfile(filename):
    """
    Return a np array containing the wav
    :param filename: Filename of wav
    """

    _, wave = wavfile.read(filename)

    # Pad with noise if audio is short
    if len(wave) < AUDIO_LENGTH:
        silence_part = np.random.normal(0, 5, AUDIO_LENGTH - len(wave))
        wave = np.append(np.asarray(wave), silence_part)

    return np.array(wave, dtype=np.float32)


def _logMelFilterbank(wave, parse_param=PARSE_PARAMS):
    """
    Computes the log Mel filterbanks
    :param wave: Audio as an array
    :param parse_param: Window Parameter
    :return: Filterbanks
    """

    fbank = logfbank(
        wave,
        samplerate=AUDIO_SR,
        winlen=float(parse_param[0]),
        winstep=float(parse_param[1]),
        highfreq=AUDIO_SR / 2,
        nfilt=int(parse_param[2]),
    )

    fbank = np.array(fbank, dtype=np.float32)

    return fbank


def _normalize(data):
    """
    Normalizes the data (z-score)
    :param data: Data to be normalized
    :return: Nomralized data
    """

    mean = np.mean(data, axis=0)
    sd = np.std(data, axis=0)

    # If Std Dev is 0
    if not sd:
        sd = 1e-7

    return (data - mean) / sd


def _parse_fn(filename, label, parse_param=PARSE_PARAMS, scale=False):
    """
    Calculates filterbank energies for a given file
    :param filename: File name
    :param label: Class label
    :param parse_param: Window parameters
    :param scale: Whether to normalize the filterbanks
    :return: Filterbanks, Label
    """

    wave = _loadfile(filename.numpy())
    fbank = _logMelFilterbank(wave, parse_param)

    if scale:
        fbank = _normalize(fbank)

    return fbank, np.asarray(label, dtype=np.int32)


def plot_confusion_matrix(y_pred, y_true, labels, display_labels):
    """
    Plots the confusion matrix for given data
    :param y_pred: Predicted labels
    :param y_true: True labels
    :param labels: Class labels integer
    :param display_labels: Class labels to display
    :return: None
    """

    cm = confusion_matrix(y_pred=y_pred, y_true=y_true, labels=labels)

    ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels).plot(
        cmap=plt.cm.Blues, values_format="d"
    )

    plt.grid(False)

    return plt


def OC_Statistics(y_pred, y_true, file_name):
    """
    Print performance statistics for One Class problem
    :param y_pred: Predicted labels
    :param y_true: True labels
    :param file_name: Plot filename
    :return: None
    """

    print("Accuracy: {:.4f}".format(accuracy_score(y_true, y_pred)))
    print("Precision: {:.4f}".format(precision_score(y_true, y_pred)))
    print("Recall: {:.4f}".format(recall_score(y_true, y_pred)))
    print("F1-score: {:.4f}".format(f1_score(y_true, y_pred)))
    print("Matthews Correlation Coefficient: {:.4f}".format(matthews_corrcoef(y_true, y_pred)))

    sns.set(font_scale=1.50)
    plot_confusion_matrix(y_pred=y_pred, y_true=y_true, labels=[-1, 1], display_labels=["Other", "Marvin"])

    plt.tight_layout()
    plt.savefig(f"../docs/results/{file_name}.png", dpi=300)
    plt.show()


def plot_history(history):
    """
    Plots and saves training history
    :param history: Training history
    :param model_name: Model name
    :return: None
    """

    sns.set()

    loss = history.history["loss"]
    val_loss = history.history["val_loss"]

    acc = history.history["sparse_categorical_accuracy"]
    val_acc = history.history["val_sparse_categorical_accuracy"]

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 8))

    ax1.plot(loss, label="Training")
    ax1.plot(val_loss, label="Validation")
    ax1.xaxis.set_major_locator(MaxNLocator(integer=True))
    ax1.set_title("Model loss")
    ax1.set_xlabel("Epoch")
    ax1.set_ylabel("Loss")
    ax1.legend()

    ax2.plot(acc, label="Training")
    ax2.plot(val_acc, label="Validation")
    ax2.xaxis.set_major_locator(MaxNLocator(integer=True))
    ax2.set_title("Accuracy")
    ax2.set_xlabel("Epoch")
    ax2.set_ylabel("Accuracy")
    ax2.legend()

    plt.tight_layout()
    plt.savefig("../docs/results/model_training.png", dpi=300)
    fig.show()

matplotlib.ticker.MaxNLocator matplotlib.pyplot.savefig matplotlib.pyplot.subplots matplotlib.pyplot.tight_layout matplotlib.pyplot.show matplotlib.pyplot.grid