# Python 3
# Copyright (c) 2017 Mycroft AI Inc.

import json
from argparse import ArgumentParser
from os.path import isfile
from typing import *

import numpy as np

from precise.params import ListenerParams

pr = ListenerParams(window_t=0.1, hop_t=0.05, buffer_t=1.5,
                    sample_rate=16000, sample_depth=2,
                    n_mfcc=13, n_filt=20, n_fft=512)

lstm_units = 20
inhibit_t = 0.4
inhibit_dist_t = 1.0
inhibit_hop_t = 0.1


def create_parser(usage: str) -> ArgumentParser:
    """
    Creates an argument parser from a condensed usage string in the format of:
    :pos_arg_name int
        This is the help message
        which can span multiple lines
    :-o --optional_arg str default_value
        The type can be any valid python type
    :-eo --extra-option
        This adds args.extra_option as a bool
        which is False by default
    """
    first_line = [i for i in usage.split('\n') if i][0]
    indent = ' ' * (len(first_line) - len(first_line.lstrip(' ')))
    usage = usage.replace('\n' + indent, '\n')

    defaults = {}
    description, *descriptors = usage.split('\n:')
    parser = ArgumentParser(description=description.strip())
    for descriptor in descriptors:
        try:
            options, *help = descriptor.split('\n')
            help = ' '.join(help).replace('    ', '')
            if options.count(' ') == 1:
                if options[0] == '-':
                    short, long = options.split(' ')
                    var_name = long.strip('-').replace('-', '_')
                    parser.add_argument(short, long, dest=var_name, action='store_true', help=help)
                    defaults[var_name] = False
                else:
                    short, typ = options.split(' ')
                    parser.add_argument(short, type=eval(typ), help=help)
            else:
                short, long, typ, default = options.split(' ')
                help += '. Default: ' + default
                default = '' if default == '-' else default
                parser.add_argument(short, long, type=eval(typ), default=default, help=help)
        except Exception as e:
            print(e.__class__.__name__ + ': ' + str(e))
            print('While parsing:')
            print(descriptor)
            exit(1)
    return parser


def buffer_to_audio(buffer: bytes) -> np.ndarray:
    """Convert a raw mono audio byte string to numpy array of floats"""
    return np.fromstring(buffer, dtype='<i2').astype(np.float32, order='C') / 32768.0


def inject_params(model_name: str) -> ListenerParams:
    params_file = model_name + '.params'
    try:
        global pr
        with open(params_file) as f:
            pr = ListenerParams(**json.load(f))
    except (OSError, ValueError, TypeError):
        print('Warning: Failed to load parameters from ' + params_file)
    return pr


def save_params(model_name: str):
    with open(model_name + '.params', 'w') as f:
        json.dump(pr._asdict(), f)


def vectorize_raw(audio: np.ndarray) -> np.ndarray:
    """Turns audio into feature vectors, without clipping for length"""
    from speechpy.feature import mfcc
    return mfcc(audio, pr.sample_rate, pr.window_t, pr.hop_t, pr.n_mfcc, pr.n_filt, pr.n_fft)


def vectorize(audio: np.ndarray) -> np.ndarray:
    """
    Args:
        audio: Audio verified to be of `sample_rate`

    Returns:
        array<float>: Vector representation of audio
    """
    if len(audio) > pr.max_samples:
        audio = audio[-pr.max_samples:]
    features = vectorize_raw(audio)
    if len(features) < pr.n_features:
        features = np.concatenate(
            [np.zeros((pr.n_features - len(features), len(features[0]))), features])
    if len(features) > pr.n_features:
        features = features[-pr.n_features:]

    return features


def vectorize_inhibit(audio: np.ndarray) -> np.ndarray:
    """
    Returns an array of inputs generated from the
    keyword audio that shouldn't cause an activation
    """

    def samp(x):
        return int(pr.sample_rate * x)

    inputs = []
    for offset in range(samp(inhibit_t), samp(inhibit_dist_t), samp(inhibit_hop_t)):
        if len(audio) - offset < samp(pr.buffer_t / 2.):
            break
        inputs.append(vectorize(audio[:-offset]))
    return np.array(inputs) if inputs else np.empty((0, pr.n_features, pr.feature_size))


def load_vector(name: str, vectorizer: Callable = vectorize) -> np.ndarray:
    """Loads and caches a vector input from a wav or npy file"""
    import os

    save_name = name if name.endswith('.npy') else os.path.join('cache', str(abs(hash(pr))),
                                                                vectorizer.__name__ + '.' + name + '.npy')

    if os.path.isfile(save_name):
        return np.load(save_name)

    print('Loading ' + name + '...')
    os.makedirs(os.path.dirname(save_name), exist_ok=True)

    vec = vectorizer(load_audio(name))
    np.save(save_name, vec)
    return vec


def load_audio(file: Any) -> np.ndarray:
    """
    Args:
        file: Audio filename or file object
    Returns:
        samples: Sample rate and audio samples from 0..1
    """
    import wavio
    wav = wavio.read(file)
    if wav.data.dtype != np.int16:
        raise ValueError('Unsupported data type: ' + str(wav.data.dtype))
    if wav.rate != pr.sample_rate:
        raise ValueError('Unsupported sample rate: ' + str(wav.rate))

    data = np.squeeze(wav.data)
    return data.astype(np.float32) / float(np.iinfo(data.dtype).max)


def save_audio(filename: str, audio: np.ndarray):
    import wavio
    save_audio = (audio * np.iinfo(np.int16).max).astype(np.int16)
    wavio.write(filename, save_audio, pr.sample_rate, sampwidth=pr.sample_depth, scale='none')


def glob_all(folder: str, filt: str) -> List[str]:
    """Recursive glob"""
    import os
    import fnmatch
    matches = []
    for root, dirnames, filenames in os.walk(folder):
        for filename in fnmatch.filter(filenames, filt):
            matches.append(os.path.join(root, filename))
    return matches


def find_wavs(folder: str) -> Tuple[List[str], List[str]]:
    """Finds keyword and not-keyword wavs in folder"""
    return glob_all(folder + '/keyword', '*.wav'), glob_all(folder + '/not-keyword', '*.wav')


def weighted_log_loss(yt, yp) -> Any:
    """
    Binary crossentropy with a bias towards false negatives
    yt: Target
    yp: Prediction
    """
    from keras import backend as K
    weight = 0.9  # [0..1] where 1 is inf bias

    pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon())
    neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon())
    return weight * K.sum(neg_loss) + (1. - weight) * K.sum(pos_loss)


def weighted_mse_loss(yt, yp) -> Any:
    from keras import backend as K
    weight = 0.9  # [0..1] where 1 is inf bias

    total = K.sum(K.ones_like(yt))
    neg_loss = total * K.sum(K.square(yp * (1 - yt))) / K.sum(1 - yt)
    pos_loss = total * K.sum(K.square(1. - (yp * yt))) / K.sum(yt)

    return weight * neg_loss + (1. - weight) * pos_loss


def false_pos(yt, yp) -> Any:
    from keras import backend as K
    return K.sum(K.cast(yp * (1 - yt) > 0.5, 'float')) / K.sum(1 - yt)


def false_neg(yt, yp) -> Any:
    from keras import backend as K
    return K.sum(K.cast((1 - yp) * (0 + yt) > 0.5, 'float')) / K.sum(0 + yt)


def load_keras() -> Any:
    import keras
    keras.losses.weighted_log_loss = weighted_log_loss
    keras.metrics.false_pos = false_pos
    keras.metrics.false_neg = false_neg
    return keras


def load_precise_model(model_name: str) -> Any:
    """Loads a Keras model from file, handling custom loss function"""
    if not model_name.endswith('.net'):
        print('Warning: Unknown model type, ', model_name)

    inject_params(model_name)
    return load_keras().models.load_model(model_name)


def create_model(model_name: str, skip_acc: bool = False) -> Any:
    """
    Load or create a precise model

    Args:
        model_name: Name of model
        skip_acc: Whether to skip accuracy calculation while training

    Returns:
        model: Loaded Keras model
    """
    if isfile(model_name):
        print('Loading from ' + model_name + '...')
        model = load_precise_model(model_name)
    else:
        from keras.layers.core import Dense
        from keras.layers.recurrent import GRU
        from keras.models import Sequential

        model = Sequential()
        model.add(GRU(lstm_units, activation='linear', input_shape=(pr.n_features, pr.feature_size),
                      dropout=0.3, name='net'))
        model.add(Dense(1, activation='sigmoid'))

    load_keras()
    metrics = ['accuracy', false_pos, false_neg]
    model.compile('rmsprop', weighted_log_loss, metrics=(not skip_acc) * metrics)
    return model