# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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 numpy as np
import tvm
from tvm import te
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.relay.testing.config import ctx_list
import keras

try:
    import tensorflow.compat.v1 as tf
except ImportError:
    import tensorflow as tf

from tensorflow import keras as tf_keras
from packaging import version as package_version
# prevent Keras from using up all gpu memory
if tf.executing_eagerly():
    gpus = tf.config.experimental.list_physical_devices('GPU')
    for gpu in gpus:
        tf.config.experimental.set_memory_growth(gpu, True)
else:
    from keras.backend.tensorflow_backend import set_session
    config = tf.ConfigProto()
    config.gpu_options.per_process_gpu_memory_fraction = 0.5
    set_session(tf.Session(config=config))


def pytest_generate_tests(metafunc):
    # This function generates the list of tests for pytest, based
    # on scenatios that will change the parameters in which the
    # tests use to run.
    # https://docs.pytest.org/en/latest/example/parametrize.html
    idlist = []
    argvalues = []
    for scenario in metafunc.cls.scenarios:
        idlist.append(scenario[0])
        items = scenario[1].items()
        argnames = [x[0] for x in items]
        argvalues.append([x[1] for x in items])
    metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class")


# Scenarios:
# - classic keras, using keras from "import keras"
# - tensorflow keras, using keras from "from tensorflow import keras as tf_keras"
using_classic_keras = ("keras", {"keras": keras})
using_tensorflow_keras = ("tf_keras", {"keras": tf_keras})


def verify_keras_frontend(keras_model, need_transpose=True, layout='NCHW'):
    # Keras frontend currently supports tensorflow backend only.
    assert(keras.backend.backend() == 'tensorflow')

    if layout != 'NCHW':
        need_transpose = False

    in_shapes = []
    for layer in keras_model._input_layers:
        if tf.executing_eagerly():
            in_shapes.append(tuple(dim if dim is not None else 1 for dim in layer.input.shape))
        else:
            in_shapes.append(tuple(dim.value if dim.value is not None else 1 for dim in layer.input.shape))


    def get_keras_output(xs, dtype='float32'):
        return keras_model.predict(xs)

    def get_tvm_output(xs, target, ctx, dtype='float32'):
        shape_dict = {name: x.shape for (name, x) in zip(keras_model.input_names, xs)}
        mod, params = relay.frontend.from_keras(keras_model, shape_dict, layout=layout)
        with relay.transform.build_config(opt_level=2):
            graph, lib, params = relay.build(mod,
                                             target,
                                             params=params)
        m = graph_runtime.create(graph, lib, ctx)
        for name, x in zip(keras_model.input_names, xs):
            m.set_input(name, tvm.nd.array(x.astype(dtype)))
        m.set_input(**params)
        m.run()
        return [m.get_output(i).asnumpy() for i in range(m.get_num_outputs())]

    def to_channels_first(arr):
        return arr.transpose([0, -1] + list(range(1, arr.ndim - 1)))

    def to_channels_last(arr):
        return arr.transpose([0] + list(range(2, arr.ndim)) + [1])

    xs = [np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes]
    keras_out = get_keras_output(xs)
    keras_out = keras_out if isinstance(keras_out, list) else [keras_out]
    for target, ctx in ctx_list():
        inputs = [to_channels_first(x) for x in xs] if need_transpose else xs
        tvm_out = get_tvm_output(inputs, target, ctx)
        for kout, tout in zip(keras_out, tvm_out):
            if need_transpose:
                tout = to_channels_last(tout)
            tvm.testing.assert_allclose(kout, tout, rtol=1e-5, atol=1e-5)


class TestKeras:
    scenarios = [using_classic_keras, using_tensorflow_keras]

    def test_forward_merge(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
        y = keras.layers.Conv2D(8, (3, 3), padding="same")(x)
        z = keras.layers.Conv2D(8, (3, 3), padding="same")(y)
        merge_funcs = [keras.layers.Add(),
                    keras.layers.Subtract(),
                    keras.layers.Multiply(),
                    keras.layers.Maximum(),
                    keras.layers.Average(),
                    keras.layers.Concatenate()]
        for merge_func in merge_funcs:
            class_name = type(merge_func).__name__
            if class_name in ('Subtract', 'Dot'):
                out = merge_func([x, y])
            else:
                out = merge_func([x, y, z])
            keras_model = keras.models.Model(data, out)
            verify_keras_frontend(keras_model)

    def test_forward_merge_dot(self, keras):
        data1 = keras.layers.Input(shape=(2, 2))
        data2 = keras.layers.Input(shape=(2, 2))
        merge_funcs = [keras.layers.Dot(axes=[1, 2]),
                    keras.layers.Dot(axes=[2, 1]),
                    keras.layers.Dot(axes=[1, 1]),
                    keras.layers.Dot(axes=[2, 2]),
                    keras.layers.Dot(axes=1),
                    keras.layers.Dot(axes=2)]
        for merge_func in merge_funcs:
            out = merge_func([data1, data2])
            keras_model = keras.models.Model([data1, data2], out)
            verify_keras_frontend(keras_model)

    def test_forward_activations(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        act_funcs = [keras.layers.Activation('softmax'),
                    keras.layers.Softmax(),
                    keras.layers.Softmax(axis=-1),
                    keras.layers.Softmax(axis=1),
                    keras.layers.Softmax(axis=2),
                    keras.layers.Softmax(axis=3),
                    keras.layers.Activation('softplus'),
                    keras.layers.Activation('relu'),
                    keras.layers.Activation('softsign'),
                    keras.layers.Activation('hard_sigmoid'),
                    keras.layers.Activation('sigmoid'),
                    keras.layers.Activation('tanh'),
                    keras.layers.Activation('linear'),
                    keras.layers.Activation('selu'),
                    keras.layers.ReLU(),
                    keras.layers.ReLU(max_value=6.),
                    keras.layers.ReLU(max_value=6., threshold=0.),
                    keras.layers.ReLU(max_value=6., threshold=1.),
                    keras.layers.ReLU(max_value=6., threshold=1., negative_slope=0.),
                    keras.layers.ReLU(max_value=6., threshold=1., negative_slope=0.5),
                    keras.layers.ReLU(max_value=6., threshold=1., negative_slope=1.),
                    keras.layers.LeakyReLU(alpha=0.3),
                    keras.layers.PReLU(weights=np.random.rand(1, 32, 32, 3)),
                    keras.layers.ELU(alpha=0.5),
                    keras.layers.ThresholdedReLU(theta=0.5)]
        for act_func in act_funcs:
            x = act_func(data)
            keras_model = keras.models.Model(data, x)
            verify_keras_frontend(keras_model)


    def test_forward_dense(self, keras):
        data = keras.layers.Input(shape=(32, 32, 1))
        x = keras.layers.Flatten()(data)
        x = keras.layers.Dropout(0.5)(x)
        x = keras.layers.Dense(10, activation='relu', kernel_initializer='uniform')(x)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)

    def test_forward_permute(self, keras):
        data = keras.layers.Input(shape=(2, 3, 4))
        x = keras.layers.Permute([2, 3, 1])(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model, need_transpose=False)

    def test_forward_sequential(self, keras):
        keras_model = keras.models.Sequential([
            keras.layers.Dense(16, input_dim=32, activation='relu'),
            keras.layers.Dropout(0.5),
            keras.layers.Dense(8, activation='relu'),
            keras.layers.Dropout(0.5),
            keras.layers.Dense(1, activation='sigmoid')
        ])
        verify_keras_frontend(keras_model)


    def test_forward_pool(self, keras):
        data = keras.layers.Input(shape=(32, 32, 1))
        # maxpool
        x = keras.layers.MaxPooling2D((3, 3), strides=(1, 1), padding='same')(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)
        # avgpool
        y = keras.layers.AveragePooling2D((3, 3), strides=(1, 1), padding='same')(data)
        keras_model = keras.models.Model(data, y)
        verify_keras_frontend(keras_model)


    def test_forward_conv(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        conv_funcs = [keras.layers.Conv2D(filters=10, kernel_size=(3, 3),
                                        strides=(2, 2), padding='same'),
                    keras.layers.Conv2D(filters=10, kernel_size=(3, 3),
                                        dilation_rate=(2, 2), padding='same'),
                    keras.layers.Conv2D(filters=1, kernel_size=(3, 3), padding='same'),
                    keras.layers.DepthwiseConv2D(kernel_size=(3, 3), padding='same'),
                    keras.layers.Conv2DTranspose(filters=10, kernel_size=(3, 3), padding='valid'),
                    keras.layers.SeparableConv2D(filters=10, kernel_size=(3, 3), padding='same')]
        for conv_func in conv_funcs:
            x = conv_func(data)
            keras_model = keras.models.Model(data, x)
            verify_keras_frontend(keras_model)

    def test_forward_batch_norm(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        batch_norm_funcs = [keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001,
                                                            center=True, scale=False,
                                                            beta_initializer='zeros',
                                                            gamma_initializer='ones',
                                                            moving_mean_initializer='zeros',
                                                            moving_variance_initializer='ones'),
                        keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001,
                                                            center=True, scale=True,
                                                            beta_initializer='zeros',
                                                            gamma_initializer='ones',
                                                            moving_mean_initializer='zeros',
                                                            moving_variance_initializer='ones'),
                        keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001,
                                                            center=False, scale=True,
                                                            beta_initializer='zeros',
                                                            gamma_initializer='ones',
                                                            moving_mean_initializer='zeros',
                                                            moving_variance_initializer='ones'),
                        keras.layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001,
                                                            center=False, scale=False,
                                                            beta_initializer='zeros',
                                                            gamma_initializer='ones',
                                                            moving_mean_initializer='zeros',
                                                            moving_variance_initializer='ones')]
        for batch_norm_func in batch_norm_funcs:
            x = batch_norm_func(data)
            keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)

    def test_forward_upsample(self, keras, interpolation='nearest'):
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.UpSampling2D(size=(3, 3), interpolation=interpolation)(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)


    def test_forward_reshape(self, keras):
        # input_shape len is 3, target_shape len is 3
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Reshape(target_shape=(16, 64, 3))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)
        # input_shape len is 3, target_shape len is 2
        data = keras.layers.Input(shape=(32, 8, 3))
        x = keras.layers.Reshape(target_shape=(256, 3))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)
        # input_shape len is 2, target_shape len is 3
        data = keras.layers.Input(shape=(256, 3))
        x = keras.layers.Reshape(target_shape=(8, 32, 3))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)
        # input_shape len is 2, target_shape len is 1
        data = keras.layers.Input(shape=(2, 8))
        x = keras.layers.Reshape(target_shape=(16,))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model, need_transpose=False)
        # input_shape len is 1, target_shape len is 2
        data = keras.layers.Input(shape=(16,))
        x = keras.layers.Reshape(target_shape=(4, 4))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model, need_transpose=False)
        # input_shape len is 2, target_shape len is 2
        data = keras.layers.Input(shape=(2, 8))
        x = keras.layers.Reshape(target_shape=(4, 4))(data)
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model, need_transpose=False)


    def test_forward_crop(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Cropping2D(cropping=((1, 1), (1, 1)))(data)
        x = keras.layers.Cropping2D(cropping=(1, 1))(x)
        x = keras.layers.Cropping2D(cropping=1)(x)
        x = keras.layers.Cropping2D(cropping=((0, 1), (1, 0)))(x)
        x = keras.layers.Cropping2D(cropping=(1, 0))(x)
        x = keras.layers.Cropping2D(cropping=0)(x)
        x = keras.layers.Add()([x, x])
        keras_model = keras.models.Model(data, x)
        verify_keras_frontend(keras_model)


    def test_forward_multi_inputs(self, keras):
        data1 = keras.layers.Input(shape=(32, 32, 3))
        data2 = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Conv2D(8, (3, 3), padding="same")(data1)
        y = keras.layers.Conv2D(8, (3, 3), padding="same")(data2)
        z = keras.layers.Average()([x, y])
        z = keras.layers.GlobalAveragePooling2D()(z)
        keras_model = keras.models.Model([data1, data2], z)
        verify_keras_frontend(keras_model)


    def test_forward_multi_outputs(self, keras):
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
        x = keras.layers.GlobalAveragePooling2D()(x)
        y = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
        y = keras.layers.GlobalAveragePooling2D()(y)
        keras_model = keras.models.Model(data, [x, y])
        verify_keras_frontend(keras_model)


    def test_forward_reuse_layers(self, keras):
        # reuse conv2d
        data = keras.layers.Input(shape=(32, 32, 3))
        conv2d = keras.layers.Conv2D(8, (3, 3), padding="same")
        x = conv2d(data)
        y = conv2d(data)
        z = keras.layers.Add()([x, y])
        z = keras.layers.GlobalAveragePooling2D()(z)
        keras_model = keras.models.Model(data, z)
        verify_keras_frontend(keras_model)
        # reuse add
        data = keras.layers.Input(shape=(32, 32, 3))
        x = keras.layers.Conv2D(8, (3, 3), padding="same")(data)
        add = keras.layers.Add()
        x = add([x, x])
        x = add([x, x])
        z = keras.layers.GlobalAveragePooling2D()(x)
        keras_model = keras.models.Model(data, z)
        verify_keras_frontend(keras_model)


    def test_forward_rnn(self,keras):
        data = keras.layers.Input(shape=(1, 32))
        rnn_funcs = [keras.layers.LSTM(units=16, return_state=False,
                        recurrent_activation='sigmoid', activation='tanh'),
                    keras.layers.SimpleRNN(units=16, return_state=False,
                        activation='tanh'),
                    keras.layers.GRU(units=16, return_state=False,
                        recurrent_activation='sigmoid', activation='tanh', reset_after=False)]
        for rnn_func in rnn_funcs:
            x = rnn_func(data)
            keras_model = keras.models.Model(data, x)
            verify_keras_frontend(keras_model, need_transpose=False)


    def test_forward_vgg16(self, keras, layout='NCHW'):
        keras_model = keras.applications.VGG16(include_top=True, weights='imagenet',
            input_shape=(224, 224, 3), classes=1000)
        verify_keras_frontend(keras_model, layout=layout)


    def test_forward_xception(self, keras, layout='NCHW'):
        keras_model = keras.applications.Xception(include_top=True, weights='imagenet',
            input_shape=(299, 299, 3), classes=1000)
        verify_keras_frontend(keras_model, layout=layout)


    def test_forward_resnet50(self, keras, layout='NCHW'):
        keras_model = keras.applications.ResNet50(include_top=True, weights='imagenet',
            input_shape=(224, 224, 3), classes=1000)
        verify_keras_frontend(keras_model, layout=layout)


    def test_forward_mobilenet(self, keras, layout='NCHW'):
        keras_model = keras.applications.MobileNet(include_top=True, weights='imagenet',
            input_shape=(224, 224, 3), classes=1000)
        verify_keras_frontend(keras_model, layout=layout)

    def test_forward_conv3d(self, keras):
        data = keras.layers.Input(shape=(32, 32, 32, 3))
        conv_funcs = [keras.layers.Conv3D(filters=10,
                                          kernel_size=(3, 3, 3),
                                          strides=(2, 2, 2),
                                          padding='same'),
                      keras.layers.Conv3D(filters=10,
                                          kernel_size=(3, 3, 3),
                                          dilation_rate=(2, 2, 2),
                                          padding='same'),
                      keras.layers.Conv3D(filters=1,
                                          kernel_size=(3, 3, 3),
                                          padding='valid',
                                          use_bias=False),
                      keras.layers.Conv3D(filters=10,
                                          kernel_size=(2, 2, 2),
                                          padding='valid'),
                    ]
        for conv_func in conv_funcs:
            x = conv_func(data)
            keras_model = keras.models.Model(data, x)
            verify_keras_frontend(keras_model, layout='NDHWC')

    def test_forward_pool3d(self, keras):
        data = keras.layers.Input(shape=(32, 32, 32, 1))
        pool_funcs = [# maxpool
                      keras.layers.MaxPooling3D(pool_size=(2, 2, 2),
                                                strides=(1, 1, 1),
                                                padding='same'),
                      keras.layers.MaxPooling3D(pool_size=(3, 3, 3),
                                                strides=(2, 2, 2),
                                                padding='valid'),
                      # avgpool
                      keras.layers.AveragePooling3D(pool_size=(3, 3, 3),
                                                    strides=(2, 2, 2),
                                                    padding='same'),
                      keras.layers.AveragePooling3D(pool_size=(2, 2, 2),
                                                    strides=(1, 1, 1),
                                                    padding='valid'),
                     ]
        for pool_func in pool_funcs:
            x = pool_func(data)
            keras_model = keras.models.Model(data, x)
            verify_keras_frontend(keras_model, layout='NDHWC')

if __name__ == '__main__':
    for k in [keras, tf_keras]:
        sut = TestKeras()
        sut.test_forward_merge_dot(keras=k)
        sut.test_forward_merge(keras=k)
        sut.test_forward_activations(keras=k)
        sut.test_forward_dense(keras=k)
        sut.test_forward_permute(keras=k)
        sut.test_forward_sequential(keras=k)
        sut.test_forward_pool(keras=k)
        sut.test_forward_conv(keras=k)
        sut.test_forward_batch_norm(keras=k)
        sut.test_forward_upsample(keras=k, interpolation='nearest')
        sut.test_forward_upsample(keras=k, interpolation='bilinear')
        sut.test_forward_reshape(keras=k)
        sut.test_forward_crop(keras=k)
        sut.test_forward_multi_inputs(keras=k)
        sut.test_forward_multi_outputs(keras=k)
        sut.test_forward_reuse_layers(keras=k)
        sut.test_forward_rnn(keras=k)
        sut.test_forward_vgg16(keras=k)
        sut.test_forward_vgg16(keras=k, layout='NHWC')
        sut.test_forward_xception(keras=k)
        sut.test_forward_resnet50(keras=k)
        sut.test_forward_resnet50(keras=k, layout='NHWC')
        sut.test_forward_mobilenet(keras=k)
        sut.test_forward_mobilenet(keras=k, layout='NHWC')
        sut.test_forward_conv3d(keras=k)
        sut.test_forward_pool3d(keras=k)
