#!/usr/bin/env python3
# Attribution: This script was adapted from https://github.com/amir-abdi/keras_to_tensorflow
# Copyright 2018 Mycroft AI Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from os.path import split, isfile
from prettyparse import create_parser
from shutil import copyfile

usage = '''
    Convert wake word model from Keras to TensorFlow
    
    :model str
        Input Keras model (.net)
    
    :-o --out str {model}.pb
        Custom output TensorFlow protobuf filename
'''


def convert(model_path: str, out_file: str):
    """
    Converts an HD5F file from Keras to a .pb for use with TensorFlow

    Args:
        model_path: location of Keras model
        out_file: location to write protobuf
    """
    print('Converting', model_path, 'to', out_file, '...')

    import tensorflow as tf
    from precise.model import load_precise_model
    from keras import backend as K

    out_dir, filename = split(out_file)
    out_dir = out_dir or '.'
    os.makedirs(out_dir, exist_ok=True)

    K.set_learning_phase(0)
    model = load_precise_model(model_path)

    out_name = 'net_output'
    tf.identity(model.output, name=out_name)
    print('Output node name:', out_name)
    print('Output folder:', out_dir)

    sess = K.get_session()

    # Write the graph in human readable
    tf.train.write_graph(sess.graph.as_graph_def(), out_dir, filename + 'txt', as_text=True)
    print('Saved readable graph to:', filename + 'txt')

    # Write the graph in binary .pb file
    from tensorflow.python.framework import graph_util
    from tensorflow.python.framework import graph_io

    cgraph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), [out_name])
    graph_io.write_graph(cgraph, out_dir, filename, as_text=False)

    if isfile(model_path + '.params'):
        copyfile(model_path + '.params', out_file + '.params')

    print('Saved graph to:', filename)

    del sess


def main():
    args = create_parser(usage).parse_args()

    model_name = args.model.replace('.net', '')
    convert(args.model, args.out.format(model=model_name))


if __name__ == '__main__':
    main()