# Copyright 2020 The TensorFlow 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.
# ==============================================================================
"""Memory profile on TF-Keras model.

To add a new model for memory profile:
1. Create the model.
2. Decorate it with `@memory_profiler.profile`.
3. Add the model function to the dict `models`.
"""

import numpy as np
from absl import app
from absl import flags
from absl import logging

import tf_keras.src as keras

try:
    import memory_profiler
except ImportError:
    memory_profiler = None


FLAGS = flags.FLAGS
flags.DEFINE_string("model", None, "The model to run memory profiler.")


def main(_):
    @memory_profiler.profile
    def _imdb_lstm_model():
        """LSTM model."""
        x_train = np.random.randint(0, 1999, size=(2500, 100))
        y_train = np.random.random((2500, 1))

        # IMDB LSTM model.
        model = keras.Sequential()
        model.add(keras.layers.Embedding(20000, 128))
        model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))
        model.add(keras.layers.Dense(1, activation="sigmoid"))

        model.compile("sgd", "mse")
        # Warm up the model with one epoch.
        model.fit(x_train, y_train, batch_size=512, epochs=3)

    # Add the model for memory profile.
    models = {
        "lstm": _imdb_lstm_model,
    }

    if FLAGS.model in models:
        logging.info("Run memory profile on %s.", FLAGS.model)
        run_model = models[FLAGS.model]
        run_model()
    else:
        logging.info("The model does not exist. Please verify the model name.")


if __name__ == "__main__":
    flags.mark_flags_as_required(["model"])
    if memory_profiler:
        app.run(main)