# Copyright 2020 Huy Le Nguyen (@usimarit)
#
# 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
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import tensorflow as tf

logger = tf.get_logger()

DEFAULT_YAML = os.path.join(os.path.abspath(os.path.dirname(__file__)), "config.yml")

from tensorflow_asr.configs.config import Config
from tensorflow_asr.models.transducer.rnn_transducer import RnnTransducer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer


def test_streaming_transducer():
    config = Config(DEFAULT_YAML)

    text_featurizer = CharFeaturizer(config.decoder_config)

    speech_featurizer = TFSpeechFeaturizer(config.speech_config)

    model = RnnTransducer(vocabulary_size=text_featurizer.num_classes, **config.model_config)

    model.make(speech_featurizer.shape)
    model.summary(line_length=150)

    model.add_featurizers(speech_featurizer=speech_featurizer, text_featurizer=text_featurizer)

    concrete_func = model.make_tflite_function(timestamp=False).get_concrete_function()
    converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.experimental_new_converter = True
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
    tflite_model = converter.convert()

    logger.info("Converted successfully with no timestamp")

    concrete_func = model.make_tflite_function(timestamp=True).get_concrete_function()
    converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
    converter.optimizations = [tf.lite.Optimize.DEFAULT]
    converter.experimental_new_converter = True
    converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
    converter.convert()

    logger.info("Converted successfully with timestamp")

    tflitemodel = tf.lite.Interpreter(model_content=tflite_model)
    signal = tf.random.normal([4000])

    input_details = tflitemodel.get_input_details()
    output_details = tflitemodel.get_output_details()
    tflitemodel.resize_tensor_input(input_details[0]["index"], signal.shape)
    tflitemodel.allocate_tensors()
    tflitemodel.set_tensor(input_details[0]["index"], signal)
    tflitemodel.set_tensor(
        input_details[1]["index"],
        tf.constant(text_featurizer.blank, dtype=tf.int32)
    )
    tflitemodel.set_tensor(
        input_details[2]["index"],
        tf.zeros(
            [config.model_config["encoder_nlayers"], 2, 1, config.model_config["encoder_rnn_units"]],
            dtype=tf.float32
        )
    )
    tflitemodel.set_tensor(
        input_details[3]["index"],
        tf.zeros(
            [config.model_config["prediction_num_rnns"], 2, 1, config.model_config["prediction_rnn_units"]],
            dtype=tf.float32
        )
    )
    tflitemodel.invoke()
    hyp = tflitemodel.get_tensor(output_details[0]["index"])

    logger.info(hyp)


if __name__ == '__main__':
    test_streaming_transducer()
