"""
Copyright 2020 The OneFlow 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.
"""

import os
import unittest
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import test_global_storage
from test_util import GenArgList

import oneflow.compatible.single_client.unittest
from oneflow.compatible import single_client as flow

gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)


def grouped_convolution1D(
    inputs, filters, padding, num_groups, strides=None, dilation_rate=None
):
    input_list = tf.split(inputs, num_groups, axis=-1)
    filter_list = tf.split(filters, num_groups, axis=-1)
    output_list = []
    for (conv_idx, (input_tensor, filter_tensor)) in enumerate(
        zip(input_list, filter_list)
    ):
        output_list.append(
            tf.nn.conv1d(
                input_tensor,
                filter_tensor,
                padding="VALID",
                stride=[1, 1, 1],
                data_format="NWC",
            )
        )
    outputs = tf.concat(output_list, axis=-1)
    return outputs


def compare_with_tensorflow(
    test_case, device_type, x_shape, filters, kernel_size, groups
):
    assert device_type in ["gpu", "cpu"]
    flow.clear_default_session()
    func_config = flow.FunctionConfig()
    func_config.default_data_type(flow.float)

    @flow.global_function(type="train", function_config=func_config)
    def ConvJob():
        with flow.scope.placement(device_type, "0:0"):
            x = flow.get_variable(
                "x",
                shape=x_shape,
                dtype=flow.float,
                initializer=flow.random_uniform_initializer(minval=0, maxval=100),
                trainable=True,
            )
            loss = flow.layers.conv1d(
                x,
                filters,
                kernel_size=kernel_size,
                strides=[1],
                padding="valid",
                data_format="NCW",
                dilation_rate=1,
                groups=groups,
                use_bias=False,
                kernel_initializer=flow.random_uniform_initializer(
                    minval=0, maxval=100
                ),
                weight_name="conv1d_weight",
            )
            weight_shape = (filters, x.shape[1] // groups, kernel_size)
            weight = flow.get_variable(
                name="conv1d_weight",
                shape=weight_shape,
                dtype=flow.float,
                initializer=flow.random_uniform_initializer(minval=0, maxval=100),
            )
            flow.optimizer.SGD(
                flow.optimizer.PiecewiseConstantScheduler([], [0.0001]), momentum=0
            ).minimize(loss)
            flow.watch(x, test_global_storage.Setter("x"))
            flow.watch_diff(x, test_global_storage.Setter("x_diff"))
            flow.watch(weight, test_global_storage.Setter("weight"))
            flow.watch_diff(weight, test_global_storage.Setter("weight_diff"))
            flow.watch(loss, test_global_storage.Setter("loss"))
            flow.watch_diff(loss, test_global_storage.Setter("loss_diff"))
            return loss

    of_out = ConvJob().get()
    with tf.GradientTape(persistent=True) as tape:
        x = tf.Variable(test_global_storage.Get("x").transpose(0, 2, 1))
        assert groups > 0
        assert x_shape[1] % groups == 0
        assert filters % groups == 0
        if groups == 1:
            weight = tf.Variable(test_global_storage.Get("weight").transpose(2, 1, 0))
            tf_out = tf.nn.conv1d(
                x, weight, stride=[1, 1, 1], padding="VALID", data_format="NWC"
            )
        else:
            weight = tf.Variable(test_global_storage.Get("weight").transpose(2, 1, 0))
            tf_out = grouped_convolution1D(
                x, weight, padding="VALID", num_groups=groups
            )
    loss_diff = test_global_storage.Get("loss_diff").transpose(0, 2, 1)
    tf_x_diff = tape.gradient(tf_out, x, loss_diff)
    tf_weight_diff = tape.gradient(tf_out, weight, loss_diff)
    of_out_np = of_out.numpy().transpose(0, 2, 1)
    tf_out_np = tf_out.numpy()
    max_abs_diff = np.max(np.absolute(of_out_np - tf_out_np))
    fail_info = "\nshape (of vs. tf): {} vs. {}\nmax_abs_diff: {}".format(
        of_out_np.shape, tf_out_np.shape, max_abs_diff
    )
    test_case.assertTrue(
        np.allclose(of_out_np, tf_out_np, rtol=1e-05, atol=1e-05), fail_info
    )
    of_x_diff_arr = test_global_storage.Get("x_diff").transpose(0, 2, 1)
    tf_x_diff_arr = tf_x_diff.numpy()
    max_abs_diff = np.max(np.abs(of_x_diff_arr - tf_x_diff_arr))
    test_case.assertTrue(
        np.allclose(of_x_diff_arr, tf_x_diff_arr, rtol=1e-05, atol=0.0001)
    )
    test_case.assertTrue(
        np.allclose(
            test_global_storage.Get("weight_diff").transpose(2, 1, 0),
            tf_weight_diff.numpy(),
            rtol=1e-05,
            atol=1e-05,
        )
    )


@flow.unittest.skip_unless_1n1d()
class TestLayersConv1d(flow.unittest.TestCase):
    def test_conv1(test_case):
        arg_dict = OrderedDict()
        arg_dict["device_type"] = ["gpu"]
        arg_dict["x_shape"] = [(10, 32, 20)]
        arg_dict["filters"] = [64]
        arg_dict["kernel_size"] = [3]
        arg_dict["groups"] = [32]
        for arg in GenArgList(arg_dict):
            compare_with_tensorflow(test_case, *arg)

    def test_conv2(test_case):
        arg_dict = OrderedDict()
        arg_dict["device_type"] = ["gpu", "cpu"]
        arg_dict["x_shape"] = [(10, 32, 20)]
        arg_dict["filters"] = [32]
        arg_dict["kernel_size"] = [3, 2]
        arg_dict["groups"] = [1]
        for arg in GenArgList(arg_dict):
            compare_with_tensorflow(test_case, *arg)


if __name__ == "__main__":
    unittest.main()
