# Copyright 2019 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.
# ==============================================================================
"""A simple functional keras model with one layer."""

import numpy as np
import tensorflow.compat.v2 as tf

import keras
from keras.distribute import model_collection_base
from keras.optimizers.legacy import gradient_descent

_BATCH_SIZE = 10


def _get_data_for_simple_models():
    x_train = tf.constant(np.random.rand(1000, 3), dtype=tf.float32)
    y_train = tf.constant(np.random.rand(1000, 5), dtype=tf.float32)
    x_predict = tf.constant(np.random.rand(1000, 3), dtype=tf.float32)

    return x_train, y_train, x_predict


class SimpleFunctionalModel(model_collection_base.ModelAndInput):
    """A simple functional model and its inputs."""

    def get_model(self, **kwargs):
        output_name = "output_1"

        x = keras.layers.Input(shape=(3,), dtype=tf.float32)
        y = keras.layers.Dense(5, dtype=tf.float32, name=output_name)(x)

        model = keras.Model(inputs=x, outputs=y)
        optimizer = gradient_descent.SGD(learning_rate=0.001)
        model.compile(loss="mse", metrics=["mae"], optimizer=optimizer)

        return model

    def get_data(self):
        return _get_data_for_simple_models()

    def get_batch_size(self):
        return _BATCH_SIZE


class SimpleSequentialModel(model_collection_base.ModelAndInput):
    """A simple sequential model and its inputs."""

    def get_model(self, **kwargs):
        output_name = "output_1"

        model = keras.Sequential()
        y = keras.layers.Dense(
            5, dtype=tf.float32, name=output_name, input_dim=3
        )
        model.add(y)
        optimizer = gradient_descent.SGD(learning_rate=0.001)
        model.compile(loss="mse", metrics=["mae"], optimizer=optimizer)

        return model

    def get_data(self):
        return _get_data_for_simple_models()

    def get_batch_size(self):
        return _BATCH_SIZE


class _SimpleModel(keras.Model):
    def __init__(self):
        super().__init__()
        self._dense_layer = keras.layers.Dense(5, dtype=tf.float32)

    def call(self, inputs):
        return self._dense_layer(inputs)


class SimpleSubclassModel(model_collection_base.ModelAndInput):
    """A simple subclass model and its data."""

    def get_model(self, **kwargs):
        model = _SimpleModel()
        optimizer = gradient_descent.SGD(learning_rate=0.001)
        model.compile(
            loss="mse", metrics=["mae"], cloning=False, optimizer=optimizer
        )

        return model

    def get_data(self):
        return _get_data_for_simple_models()

    def get_batch_size(self):
        return _BATCH_SIZE


class _SimpleModule(tf.Module):
    def __init__(self):
        self.v = tf.Variable(3.0)

    @tf.function
    def __call__(self, x):
        return self.v * x


class SimpleTFModuleModel(model_collection_base.ModelAndInput):
    """A simple model based on tf.Module and its data."""

    def get_model(self, **kwargs):
        model = _SimpleModule()
        return model

    def get_data(self):
        return _get_data_for_simple_models()

    def get_batch_size(self):
        return _BATCH_SIZE