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

   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
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   - Redistributions of source code must retain the above copyright
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   - Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   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
   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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'''

# Train an LPCNet model
import tensorflow as tf
strategy = tf.distribute.MultiWorkerMirroredStrategy()


import argparse
#from plc_loader import PLCLoader

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

parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
parser.add_argument('output', metavar='<output>', help='trained model file (.h5)')
parser.add_argument('--model', metavar='<model>', default='rdovae', 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('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
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('--logdir', metavar='<log dir>', help='directory for tensorboard log files')


args = parser.parse_args()

import importlib
rdovae = 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

#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


opt = Adam(lr, decay=decay, beta_2=0.99)

with strategy.scope():
    model, encoder, decoder, _ = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size, nb_quant=16)
    model.compile(optimizer=opt, loss=[rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.sq1_rate_loss, rdovae.sq2_rate_loss], loss_weights=[.5, .5, 1., .1], metrics={'hard_bits':rdovae.sq_rate_metric})
    model.summary()

lpc_order = 16

feature_file = args.features
nb_features = model.nb_used_features + lpc_order
nb_used_features = model.nb_used_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))
print(features.shape)
features = features[:, :, :nb_used_features]

#lambda_val = np.repeat(np.random.uniform(.0007, .002, (features.shape[0], 1, 1)), features.shape[1]//2, axis=1)
#quant_id = np.round(10*np.log(lambda_val/.0007)).astype('int16')
#quant_id = quant_id[:,:,0]
quant_id = np.repeat(np.random.randint(16, size=(features.shape[0], 1, 1), dtype='int16'), features.shape[1]//2, axis=1)
lambda_val = .0002*np.exp(quant_id/3.8)
quant_id = quant_id[:,:,0]

# dump models to disk as we go
checkpoint = ModelCheckpoint('{}_{}_{}.h5'.format(args.output, args.cond_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.cond_size))

callbacks = [checkpoint]
#callbacks = []

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

model.fit([features, quant_id, lambda_val], [features, features, features, features], batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=callbacks)