#!/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
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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('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('--weights', metavar='<input weights>', help='model weights')
parser.add_argument('--cond-size', metavar='<units>', default=1024, type=int, help='number of units in conditioning network (default 1024)')
parser.add_argument('--batch-size', metavar='<batch size>', default=1, 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)')


args = parser.parse_args()

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

from rdovae import apply_dead_zone

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
from rdovae import pvq_quantize

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

model, encoder, decoder, qembedding = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)
model.load_weights(args.weights)

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]
#features = np.random.randn(73600, 1000, 17)


bits, gru_state_dec = encoder.predict([features], batch_size=batch_size)
(gru_state_dec).astype('float32').tofile(args.output + "-state.f32")


#dist = rdovae.feat_dist_loss(features, quant_out)
#rate = rdovae.sq1_rate_loss(features, model_bits)
#rate2 = rdovae.sq_rate_metric(features, model_bits)
#print(dist, rate, rate2)

print("shapes are:")
print(bits.shape)
print(gru_state_dec.shape)

features.astype('float32').tofile(args.output + "-input.f32")
#quant_out.astype('float32').tofile(args.output + "-enc_dec.f32")
nbits=80
bits.astype('float32').tofile(args.output + "-syms.f32")

lambda_val = 0.0002 * np.ones((nb_sequences, sequence_size//2, 1))
quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16')
quant_id = quant_id[:,:,0]
quant_embed = qembedding(quant_id)
quant_scale = tf.math.softplus(quant_embed[:,:,:nbits])
dead_zone = tf.math.softplus(quant_embed[:, :, nbits : 2 * nbits])

bits = bits*quant_scale
bits = np.round(apply_dead_zone([bits, dead_zone]).numpy())
bits = bits/quant_scale

gru_state_dec = pvq_quantize(gru_state_dec, 82)
#gru_state_dec = gru_state_dec/(1e-15+tf.norm(gru_state_dec, axis=-1,keepdims=True))
gru_state_dec = gru_state_dec[:,-1,:]
dec_out = decoder([bits[:,1::2,:], gru_state_dec])

print(dec_out.shape)

dec_out.numpy().astype('float32').tofile(args.output + "-quant_out.f32")