# Copyright 2018 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.
# ==============================================================================
"""Keras models for use in Model subclassing tests."""

import keras
from keras.testing_infra import test_utils


class SimpleConvTestModel(keras.Model):
    def __init__(self, num_classes=10):
        super().__init__(name="test_model")
        self.num_classes = num_classes

        self.conv1 = keras.layers.Conv2D(32, (3, 3), activation="relu")
        self.flatten = keras.layers.Flatten()
        self.dense1 = keras.layers.Dense(num_classes, activation="softmax")

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        return self.dense1(x)


def get_multi_io_subclass_model(use_bn=False, use_dp=False, num_classes=(2, 3)):
    """Creates MultiIOModel for the tests of subclass model."""
    shared_layer = keras.layers.Dense(32, activation="relu")
    branch_a = [shared_layer]
    if use_dp:
        branch_a.append(keras.layers.Dropout(0.5))
    branch_a.append(keras.layers.Dense(num_classes[0], activation="softmax"))

    branch_b = [shared_layer]
    if use_bn:
        branch_b.append(keras.layers.BatchNormalization())
    branch_b.append(keras.layers.Dense(num_classes[1], activation="softmax"))

    model = test_utils._MultiIOSubclassModel(
        branch_a, branch_b, name="test_model"
    )
    return model


class NestedTestModel1(keras.Model):
    """A model subclass nested inside a model subclass."""

    def __init__(self, num_classes=2):
        super().__init__(name="nested_model_1")
        self.num_classes = num_classes
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(num_classes, activation="relu")
        self.bn = keras.layers.BatchNormalization()
        self.test_net = test_utils.SmallSubclassMLP(
            num_hidden=32, num_classes=4, use_bn=True, use_dp=True
        )

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.bn(x)
        x = self.test_net(x)
        return self.dense2(x)


class NestedTestModel2(keras.Model):
    """A model subclass with a functional-API graph network inside."""

    def __init__(self, num_classes=2):
        super().__init__(name="nested_model_2")
        self.num_classes = num_classes
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(num_classes, activation="relu")
        self.bn = self.bn = keras.layers.BatchNormalization()
        self.test_net = self.get_functional_graph_model(32, 4)

    @staticmethod
    def get_functional_graph_model(input_dim, num_classes):
        # A simple functional-API model (a.k.a. graph network)
        inputs = keras.Input(shape=(input_dim,))
        x = keras.layers.Dense(32, activation="relu")(inputs)
        x = keras.layers.BatchNormalization()(x)
        outputs = keras.layers.Dense(num_classes)(x)
        return keras.Model(inputs, outputs)

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.bn(x)
        x = self.test_net(x)
        return self.dense2(x)


def get_nested_model_3(input_dim, num_classes):
    # A functional-API model with a subclassed model inside.
    # NOTE: this requires the inner subclass to implement
    # `compute_output_shape`.

    inputs = keras.Input(shape=(input_dim,))
    x = keras.layers.Dense(32, activation="relu")(inputs)
    x = keras.layers.BatchNormalization()(x)

    class Inner(keras.Model):
        def __init__(self):
            super().__init__()
            self.dense1 = keras.layers.Dense(32, activation="relu")
            self.dense2 = keras.layers.Dense(5, activation="relu")
            self.bn = keras.layers.BatchNormalization()

        def call(self, inputs):
            x = self.dense1(inputs)
            x = self.dense2(x)
            return self.bn(x)

    test_model = Inner()
    x = test_model(x)
    outputs = keras.layers.Dense(num_classes)(x)
    return keras.Model(inputs, outputs, name="nested_model_3")


class CustomCallModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(1, activation="relu")
        self.dense2 = keras.layers.Dense(1, activation="softmax")

    def call(self, first, second, fiddle_with_output="no", training=True):
        combined = self.dense1(first) + self.dense2(second)
        if fiddle_with_output == "yes":
            return 10.0 * combined
        else:
            return combined


class TrainingNoDefaultModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(1)

    def call(self, x, training):
        return self.dense1(x)


class TrainingMaskingModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(1)

    def call(self, x, training=False, mask=None):
        return self.dense1(x)