#!/usr/bin/python3
'''Copyright (c) 2021-2022 Amazon
   Copyright (c) 2018-2019 Mozilla

   Redistribution and use in source and binary forms, with or without
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   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
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   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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'''

# Train an LPCNet model

import argparse
from plc_loader import PLCLoader

parser = argparse.ArgumentParser(description='Train a PLC model')

parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
parser.add_argument('lost_file', metavar='<packet loss file>', help='packet loss traces (int8)')
parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
parser.add_argument('--model', metavar='<model>', default='lpcnet_plc', help='PLC model python definition (without .py)')
group1 = parser.add_mutually_exclusive_group()
group1.add_argument('--quantize', metavar='<input weights>', help='quantize model')
group1.add_argument('--retrain', metavar='<input weights>', help='continue training model')
parser.add_argument('--gru-size', metavar='<units>', default=256, type=int, help='number of units in GRU (default 256)')
parser.add_argument('--cond-size', metavar='<units>', default=128, type=int, help='number of units in conditioning network (default 128)')
parser.add_argument('--epochs', metavar='<epochs>', default=120, type=int, help='number of epochs to train for (default 120)')
parser.add_argument('--batch-size', metavar='<batch size>', default=128, type=int, help='batch size to use (default 128)')
parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')
parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate')
parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')
parser.add_argument('--band-loss', metavar='<weight>', default=1.0, type=float, help='weight of band loss (default 1.0)')
parser.add_argument('--loss-bias', metavar='<bias>', default=0.0, type=float, help='loss bias towards low energy (default 0.0)')
parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')


args = parser.parse_args()

import importlib
lpcnet = importlib.import_module(args.model)

import sys
import numpy as np
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
import tensorflow.keras.backend as K
import h5py

import tensorflow as tf
#gpus = tf.config.experimental.list_physical_devices('GPU')
#if gpus:
#  try:
#    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
#  except RuntimeError as e:
#    print(e)

nb_epochs = args.epochs

# Try reducing batch_size if you run out of memory on your GPU
batch_size = args.batch_size

quantize = args.quantize is not None
retrain = args.retrain is not None

if quantize:
    lr = 0.00003
    decay = 0
    input_model = args.quantize
else:
    lr = 0.001
    decay = 2.5e-5

if args.lr is not None:
    lr = args.lr

if args.decay is not None:
    decay = args.decay

if retrain:
    input_model = args.retrain

def plc_loss(alpha=1.0, bias=0.):
    def loss(y_true,y_pred):
        mask = y_true[:,:,-1:]
        y_true = y_true[:,:,:-1]
        e = (y_pred - y_true)*mask
        e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')
        bias_mask = K.minimum(1., K.maximum(0., 4*y_true[:,:,-1:]))
        l1_loss = K.mean(K.abs(e)) + 0.1*K.mean(K.maximum(0., -e[:,:,-1:])) + alpha*K.mean(K.abs(e_bands) + bias*bias_mask*K.maximum(0., e_bands)) + K.mean(K.minimum(K.abs(e[:,:,18:19]),1.)) + 8*K.mean(K.minimum(K.abs(e[:,:,18:19]),.4))
        return l1_loss
    return loss

def plc_l1_loss():
    def L1_loss(y_true,y_pred):
        mask = y_true[:,:,-1:]
        y_true = y_true[:,:,:-1]
        e = (y_pred - y_true)*mask
        l1_loss = K.mean(K.abs(e))
        return l1_loss
    return L1_loss

def plc_ceps_loss():
    def ceps_loss(y_true,y_pred):
        mask = y_true[:,:,-1:]
        y_true = y_true[:,:,:-1]
        e = (y_pred - y_true)*mask
        l1_loss = K.mean(K.abs(e[:,:,:-2]))
        return l1_loss
    return ceps_loss

def plc_band_loss():
    def L1_band_loss(y_true,y_pred):
        mask = y_true[:,:,-1:]
        y_true = y_true[:,:,:-1]
        e = (y_pred - y_true)*mask
        e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')
        l1_loss = K.mean(K.abs(e_bands))
        return l1_loss
    return L1_band_loss

def plc_pitch_loss():
    def pitch_loss(y_true,y_pred):
        mask = y_true[:,:,-1:]
        y_true = y_true[:,:,:-1]
        e = (y_pred - y_true)*mask
        l1_loss = K.mean(K.minimum(K.abs(e[:,:,18:19]),.4))
        return l1_loss
    return pitch_loss

opt = Adam(lr, decay=decay, beta_2=0.99)
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()

with strategy.scope():
    model = lpcnet.new_lpcnet_plc_model(rnn_units=args.gru_size, batch_size=batch_size, training=True, quantize=quantize, cond_size=args.cond_size)
    model.compile(optimizer=opt, loss=plc_loss(alpha=args.band_loss, bias=args.loss_bias), metrics=[plc_l1_loss(), plc_ceps_loss(), plc_band_loss(), plc_pitch_loss()])
    model.summary()

lpc_order = 16

feature_file = args.features
nb_features = model.nb_used_features + lpc_order + model.nb_burg_features
nb_used_features = model.nb_used_features
nb_burg_features = model.nb_burg_features
sequence_size = args.seq_length

# u for unquantised, load 16 bit PCM samples and convert to mu-law


features = np.memmap(feature_file, dtype='float32', mode='r')
nb_sequences = len(features)//(nb_features*sequence_size)//batch_size*batch_size
features = features[:nb_sequences*sequence_size*nb_features]

features = np.reshape(features, (nb_sequences, sequence_size, nb_features))

features = features[:, :, :nb_used_features+model.nb_burg_features]

lost = np.memmap(args.lost_file, dtype='int8', mode='r')

# dump models to disk as we go
checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.gru_size, '{epoch:02d}'))

if args.retrain is not None:
    model.load_weights(args.retrain)

if quantize or retrain:
    #Adapting from an existing model
    model.load_weights(input_model)

model.save_weights('{}_{}_initial.h5'.format(args.output, args.gru_size))

loader = PLCLoader(features, lost, nb_burg_features, batch_size)

callbacks = [checkpoint]
if args.logdir is not None:
    logdir = '{}/{}_{}_logs'.format(args.logdir, args.output, args.gru_size)
    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
    callbacks.append(tensorboard_callback)

model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks)