"""
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
from typing import Dict

import numpy as np
from test_util import GenArgList

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


def _compare_l1loss_with_np(
    input_shape, target_shape, device_type, machine_ids, device_counts
):
    input = np.random.random(size=input_shape).astype(np.float32)
    target = np.random.random(size=target_shape).astype(np.float32)
    assert device_type in ["cpu", "gpu"]
    func_config = flow.FunctionConfig()
    flow.clear_default_session()
    if device_type == "cpu":
        flow.config.cpu_device_num(device_counts)
    else:
        flow.config.gpu_device_num(device_counts)
    func_config.default_placement_scope(flow.scope.placement(device_type, machine_ids))
    func_config.default_logical_view(flow.scope.consistent_view())

    def np_l1loss(np_input, np_target):
        np_l1 = np.abs(np_target - np_input)
        np_l1_mean = np.mean(np_l1)
        np_l1_sum = np.sum(np_l1)
        np_l1_dict = {
            "np_l1_loss": np_l1,
            "np_l1_loss_mean": np_l1_mean,
            "np_l1_loss_sum": np_l1_sum,
        }
        return np_l1_dict

    def np_l1_loss_diff(np_input, np_target):
        original_shape = np_target.shape
        elemcnt = np_target.size
        prediction = np_input.reshape(-1)
        label = np_target.reshape(-1)
        prediction_grad = np.zeros(elemcnt).astype(prediction.dtype)
        for i in np.arange(elemcnt):
            diff = prediction[i] - label[i]
            prediction_grad[i] = np.sign(diff)
        grad_mean = prediction_grad.reshape(original_shape) / elemcnt
        grad_dict = {"np_grad_mean": grad_mean}
        return grad_dict

    np_out_l1loss_dict = np_l1loss(input, target)
    np_grad_dict = np_l1_loss_diff(input, target)

    def assert_prediction_grad(blob: tp.Numpy):
        assert np.allclose(blob, np_grad_dict["np_grad_mean"])

    @flow.global_function(type="train", function_config=func_config)
    def oneflow_l1loss(
        of_input: tp.Numpy.Placeholder(shape=input.shape),
        of_target: tp.Numpy.Placeholder(shape=target.shape),
    ) -> Dict[str, tp.Numpy]:
        with flow.scope.placement(device_type, "0:0"):
            v = flow.get_variable(
                shape=target.shape,
                dtype=flow.float32,
                initializer=flow.constant_initializer(0),
                name="v",
            )
            x_var = of_input + v
        flow.watch_diff(x_var, assert_prediction_grad)
        l1loss = flow.nn.L1Loss(x_var, of_target, reduction="none", name="of_l1loss")
        l1loss_mean = flow.nn.L1Loss(
            x_var, of_target, reduction="mean", name="of_l1loss_mean"
        )
        l1loss_sum = flow.nn.L1Loss(
            x_var, of_target, reduction="sum", name="of_l1loss_sum"
        )
        with flow.scope.placement(device_type, "0:0"):
            flow.optimizer.SGD(
                flow.optimizer.PiecewiseConstantScheduler([], [0.001]), momentum=0
            ).minimize(l1loss_mean)
        return {
            "of_l1_loss": l1loss,
            "of_l1_loss_mean": l1loss_mean,
            "of_l1_loss_sum": l1loss_sum,
        }

    of_out_l1loss_dict = oneflow_l1loss(input, target)
    assert np.allclose(
        of_out_l1loss_dict["of_l1_loss"], np_out_l1loss_dict["np_l1_loss"]
    )
    assert np.allclose(
        of_out_l1loss_dict["of_l1_loss_mean"], np_out_l1loss_dict["np_l1_loss_mean"]
    )
    assert np.allclose(
        of_out_l1loss_dict["of_l1_loss_sum"], np_out_l1loss_dict["np_l1_loss_sum"]
    )


def _gen_arg_dict(shape, device_type, machine_ids, device_counts):
    arg_dict = OrderedDict()
    arg_dict["input_shape"] = [shape]
    arg_dict["target_shape"] = [shape]
    arg_dict["device_type"] = [device_type]
    arg_dict["machine_ids"] = [machine_ids]
    arg_dict["device_counts"] = [device_counts]
    return arg_dict


@flow.unittest.skip_unless_1n1d()
class Testl1loss1n1d(flow.unittest.TestCase):
    def test_l1loss_cpu(test_case):
        arg_dict = _gen_arg_dict(
            shape=(16, 3), device_type="cpu", machine_ids="0:0", device_counts=1
        )
        for arg in GenArgList(arg_dict):
            _compare_l1loss_with_np(*arg)

    @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
    def test_l1loss_gpu(test_case):
        arg_dict = _gen_arg_dict(
            shape=(3, 16, 32), device_type="gpu", machine_ids="0:0", device_counts=1
        )
        for arg in GenArgList(arg_dict):
            _compare_l1loss_with_np(*arg)


@flow.unittest.skip_unless_1n2d()
class Testl1loss1n2d(flow.unittest.TestCase):
    @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
    def test_l1loss_gpu_1n2d(test_case):
        arg_dict = _gen_arg_dict(
            shape=(3, 32, 16), device_type="gpu", machine_ids="0:0-1", device_counts=2
        )
        for arg in GenArgList(arg_dict):
            _compare_l1loss_with_np(*arg)


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