# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
import numpy as np

from deepspeech.models.network import DeepSpeech2

if __name__ == '__main__':

    batch_size = 2
    feat_dim = 161
    max_len = 100
    audio = np.random.randn(batch_size, feat_dim, max_len)
    audio_len = np.random.randint(100, size=batch_size, dtype='int32')
    audio_len[-1] = 100
    text = np.array([[1, 2], [1, 2]], dtype='int32')
    text_len = np.array([2] * batch_size, dtype='int32')

    place = paddle.CUDAPlace(0)
    audio = paddle.to_tensor(
        audio, dtype='float32', place=place, stop_gradient=True)
    audio_len = paddle.to_tensor(
        audio_len, dtype='int64', place=place, stop_gradient=True)
    text = paddle.to_tensor(
        text, dtype='int32', place=place, stop_gradient=True)
    text_len = paddle.to_tensor(
        text_len, dtype='int64', place=place, stop_gradient=True)

    print(audio.shape)
    print(audio_len.shape)
    print(text.shape)
    print(text_len.shape)
    print("-----------------")

    model = DeepSpeech2(
        feat_size=feat_dim,
        dict_size=10,
        num_conv_layers=2,
        num_rnn_layers=3,
        rnn_size=1024,
        use_gru=False,
        share_rnn_weights=False, )
    logits, probs, logits_len = model(audio, text, audio_len, text_len)
    print('probs.shape', probs.shape)
    print("-----------------")

    model2 = DeepSpeech2(
        feat_size=feat_dim,
        dict_size=10,
        num_conv_layers=2,
        num_rnn_layers=3,
        rnn_size=1024,
        use_gru=True,
        share_rnn_weights=False, )
    logits, probs, logits_len = model2(audio, text, audio_len, text_len)
    print('probs.shape', probs.shape)
    print("-----------------")

    model3 = DeepSpeech2(
        feat_size=feat_dim,
        dict_size=10,
        num_conv_layers=2,
        num_rnn_layers=3,
        rnn_size=1024,
        use_gru=False,
        share_rnn_weights=True, )
    logits, probs, logits_len = model3(audio, text, audio_len, text_len)
    print('probs.shape', probs.shape)
    print("-----------------")

    model4 = DeepSpeech2(
        feat_size=feat_dim,
        dict_size=10,
        num_conv_layers=2,
        num_rnn_layers=3,
        rnn_size=1024,
        use_gru=True,
        share_rnn_weights=True, )
    logits, probs, logits_len = model4(audio, text, audio_len, text_len)
    print('probs.shape', probs.shape)
    print("-----------------")

    model5 = DeepSpeech2(
        feat_size=feat_dim,
        dict_size=10,
        num_conv_layers=2,
        num_rnn_layers=3,
        rnn_size=1024,
        use_gru=False,
        share_rnn_weights=False, )
    logits, probs, logits_len = model5(audio, text, audio_len, text_len)
    print('probs.shape', probs.shape)
    print("-----------------")