#!/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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'''

# Train an LPCNet model

import argparse
from plc_loader import PLCLoader

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

parser.add_argument('weights', metavar='<weights file>', help='weights file (.h5)')
parser.add_argument('features', metavar='<features file>', help='binary features file (float32)')
parser.add_argument('output', metavar='<output>', help='reconstructed file (float32)')
parser.add_argument('--model', metavar='<model>', default='lpcnet_plc', help='PLC model python definition (without .py)')
group1 = parser.add_mutually_exclusive_group()

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)')


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)

model = lpcnet.new_lpcnet_plc_model(rnn_units=args.gru_size, batch_size=1, training=False, quantize=False, cond_size=args.cond_size)
model.compile()

lpc_order = 16

feature_file = args.features
nb_features = model.nb_used_features + lpc_order
nb_used_features = model.nb_used_features

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

features = np.loadtxt(feature_file)
print(features.shape)
sequence_size = features.shape[0]
lost = np.reshape(features[:,-1:], (1, sequence_size, 1))
features = features[:,:nb_used_features]
features = np.reshape(features, (1, sequence_size, nb_used_features))


model.load_weights(args.weights)

features = features*lost
out = model.predict([features, lost])

out = features + (1-lost)*out

np.savetxt(args.output, out[0,:,:])